Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

RESEARCH THEMATICANALYSISMETHOD

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

Print Friendly, PDF & Email

  • Bipolar Disorder
  • Therapy Center
  • When To See a Therapist
  • Types of Therapy
  • Best Online Therapy
  • Best Couples Therapy
  • Best Family Therapy
  • Managing Stress
  • Sleep and Dreaming
  • Understanding Emotions
  • Self-Improvement
  • Healthy Relationships
  • Student Resources
  • Personality Types
  • Guided Meditations
  • Verywell Mind Insights
  • 2023 Verywell Mind 25
  • Mental Health in the Classroom
  • Editorial Process
  • Meet Our Review Board
  • Crisis Support

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.

research methodology quantitative and qualitative

  • 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.

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

A woman sits at a library table with stacks of books and a laptop.

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”

Request Information

News alert: UC Berkeley has announced its next university librarian

Secondary menu

  • Log in to your Library account
  • Hours and Maps
  • Connect from Off Campus
  • UC Berkeley Home

Search form

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
  • Evidence Synthesis/Systematic Reviews
  • Get Data, Get Help!

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

Elsevier QRcode Wechat

  • Research Process

Choosing the Right Research Methodology: A Guide for Researchers

  • 3 minute read

Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

Why is data validation important in research

Why is data validation important in research?

Importance-of-Data-Collection

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

You may also like.

what is a descriptive research design

Descriptive Research Design and Its Myriad Uses

Doctor doing a Biomedical Research Paper

Five Common Mistakes to Avoid When Writing a Biomedical Research Paper

research methodology quantitative and qualitative

Making Technical Writing in Environmental Engineering Accessible

Risks of AI-assisted Academic Writing

To Err is Not Human: The Dangers of AI-assisted Academic Writing

Importance-of-Data-Collection

Writing a good review article

Scholarly Sources What are They and Where can You Find Them

Scholarly Sources: What are They and Where can You Find Them?

Input your search keywords and press Enter.

SkillsYouNeed

  • LEARNING SKILLS
  • Research Methods

Quantitative and Qualitative Research Methods

Search SkillsYouNeed:

Learning Skills:

  • A - Z List of Learning Skills
  • What is Learning?
  • Learning Approaches
  • Learning Styles
  • 8 Types of Learning Styles
  • Understanding Your Preferences to Aid Learning
  • Lifelong Learning
  • Decisions to Make Before Applying to University
  • Top Tips for Surviving Student Life
  • Living Online: Education and Learning
  • 8 Ways to Embrace Technology-Based Learning Approaches
  • Critical Thinking Skills
  • Critical Thinking and Fake News
  • Understanding and Addressing Conspiracy Theories
  • Critical Analysis
  • Study Skills
  • Exam Skills
  • Writing a Dissertation or Thesis
  • Introduction to Research Methods
  • Designing Research
  • Qualitative Research Designs
  • Interviews for Research
  • Focus Groups
  • Qualitative Data from Interactions
  • Quantitative Research Designs
  • Sampling and Sample Design
  • Surveys and Survey Design
  • Observational Research and Secondary Data
  • Analysing Research Data

Analysing Qualitative Data

  • Simple Statistical Analysis
  • Statistical Analysis: Types of Data
  • Understanding Correlations
  • Understanding Statistical Distributions
  • Significance and Confidence Intervals
  • Developing and Testing Hypotheses
  • Multivariate Analysis

Get the SkillsYouNeed Research Methods eBook

Research Methods

Part of the Skills You Need Guide for Students .

  • Teaching, Coaching, Mentoring and Counselling
  • Employability Skills for Graduates

Subscribe to our FREE newsletter and start improving your life in just 5 minutes a day.

You'll get our 5 free 'One Minute Life Skills' and our weekly newsletter.

We'll never share your email address and you can unsubscribe at any time.

Research methods are split broadly into quantitative and qualitative methods.

Which you choose will depend on your research questions, your underlying philosophy of research, and your preferences and skills.

Our pages Introduction to Research Methods and Designing Research set out some of the issues about the underlying philosophy.

This page provides an introduction to the broad principles of qualitative and quantitative research methods, and the advantages and disadvantages of each in particular situations.

Some definitions

Quantitative research is “ explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics). ”*

Qualitative research seeks to answer questions about why and how people behave in the way that they do. It provides in-depth information about human behaviour.

* Taken from: Aliaga and Gunderson ‘Interactive Statistics ‘3rd Edition (2005)

Quantitative Research

Quantitative research is perhaps the simpler to define and identify..

The data produced are always numerical, and they are analysed using mathematical and statistical methods. If there are no numbers involved, then it’s not quantitative research.

Some phenomena obviously lend themselves to quantitative analysis because they are already available as numbers. Examples include changes in achievement at various stages of education, or the increase in number of senior managers holding management degrees. However, even phenomena that are not obviously numerical in nature can be examined using quantitative methods.

Example: turning opinions into numbers

If you wish to carry out statistical analysis of the opinions of a group of people about a particular issue or element of their lives, you can ask them to express their relative agreement with statements and answer on a five- or seven-point scale, where 1 is strongly disagree, 2 is disagree, 3 is neutral, 4 is agree and 5 is strongly agree (the seven-point scale also has slightly agree/disagree).

Such scales are called Likert scales , and enable statements of opinion to be directly translated into numerical data.

The development of Likert scales and similar techniques mean that most phenomena can be studied using quantitative techniques.

This is particularly useful if you are in an environment where numbers are highly valued and numerical data is considered the ‘gold standard’.

However, it is important to note that quantitative methods are not necessarily the most suitable methods for investigation. They are unlikely to be very helpful when you want to understand the detailed reasons for particular behaviour in depth. It is also possible that assigning numbers to fairly abstract constructs such as personal opinions risks making them spuriously precise.

Sources of Quantitative Data

The most common sources of quantitative data include:

Surveys , whether conducted online, by phone or in person. These rely on the same questions being asked in the same way to a large number of people;

Observations , which may either involve counting the number of times that a particular phenomenon occurs, such as how often a particular word is used in interviews, or coding observational data to translate it into numbers; and

Secondary data , such as company accounts.

Our pages on Survey Design and Observational Research provide more information about these techniques.

Analysing Quantitative Data

There are a wide range of statistical techniques available to analyse quantitative data, from simple graphs to show the data through tests of correlations between two or more items, to statistical significance. Other techniques include cluster analysis, useful for identifying relationships between groups of subjects where there is no obvious hypothesis, and hypothesis testing, to identify whether there are genuine differences between groups.

Our page Statistical Analysis provides more information about some of the simpler statistical techniques.

Qualitative Research

Qualitative research is any which does not involve numbers or numerical data..

It often involves words or language, but may also use pictures or photographs and observations.

Almost any phenomenon can be examined in a qualitative way, and it is often the preferred method of investigation in the UK and the rest of Europe; US studies tend to use quantitative methods, although this distinction is by no means absolute.

Qualitative analysis results in rich data that gives an in-depth picture and it is particularly useful for exploring how and why things have happened.

However, there are some pitfalls to qualitative research, such as:

If respondents do not see a value for them in the research, they may provide inaccurate or false information. They may also say what they think the researcher wishes to hear. Qualitative researchers therefore need to take the time to build relationships with their research subjects and always be aware of this potential.

Although ethics are an issue for any type of research, there may be particular difficulties with qualitative research because the researcher may be party to confidential information. It is important always to bear in mind that you must do no harm to your research subjects.

It is generally harder for qualitative researchers to remain apart from their work. By the nature of their study, they are involved with people. It is therefore helpful to develop habits of reflecting on your part in the work and how this may affect the research. See our page on Reflective Practice for more.

Sources of Qualitative Data

Although qualitative data is much more general than quantitative, there are still a number of common techniques for gathering it. These include:

Interviews , which may be structured, semi-structured or unstructured;

Focus groups , which involve multiple participants discussing an issue;

‘Postcards’ , or small-scale written questionnaires that ask, for example, three or four focused questions of participants but allow them space to write in their own words;

Secondary data , including diaries, written accounts of past events, and company reports; and

Observations , which may be on site, or under ‘laboratory conditions’, for example, where participants are asked to role-play a situation to show what they might do.

Our pages on Interviews for Research , Focus Groups and Observational Research provide more information about these techniques.

Because qualitative data are drawn from a wide variety of sources, they can be radically different in scope.

There are, therefore, a wide variety of methods for analysing them, many of which involve structuring and coding the data into groups and themes. There are also a variety of computer packages to support qualitative data analysis. The best way to work out which ones are right for your research is to discuss it with academic colleagues and your supervisor.

Our page Analysing Qualitative Data provides more information about some of the most common methods.

It’s your research…

Finally, it is important to say that there is no right and wrong answer to which methods you choose.

Sometimes you may wish to use one single method, whether quantitative or qualitative, and sometimes you may want to use several, whether all one type or a mixture. It is your research and only you can decide which methods will suit both your research questions and your skills, even though you may wish to seek advice from others.

Continue to: Sampling and Sample Design Interviews for Research

See also: Writing a Research Proposal | Writing a Methodology Analysing Qualitative Data | Simple Statistical Analysis

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

  • Knowledge Base

Methodology

  • What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

  • How does social media shape body image in teenagers?
  • How do children and adults interpret healthy eating in the UK?
  • What factors influence employee retention in a large organization?
  • How is anxiety experienced around the world?
  • How can teachers integrate social issues into science curriculums?

Table of contents

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

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

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

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

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

  • Academic style
  • Vague sentences
  • Style consistency

See an example

research methodology quantitative and qualitative

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

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

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

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

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

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

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

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

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

  • Flexibility

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

  • Natural settings

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

  • Meaningful insights

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

  • Generation of new ideas

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

Prevent plagiarism. Run a free check.

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

  • Unreliability

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

  • Subjectivity

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

  • Limited generalizability

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

  • Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved April 12, 2024, from https://www.scribbr.com/methodology/qualitative-research/

Is this article helpful?

Pritha Bhandari

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, unlimited academic ai-proofreading.

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

The differences between qualitative and quantitative research methods

Last updated

15 January 2023

Reviewed by

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.

All your data in one place

Analyze your qualitative and quantitative data together in Dovetail and uncover deeper insights

  • 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. 

research methodology quantitative and qualitative

Learn more about qualitative research data analysis software

Get started today.

Go from raw data to valuable insights with a flexible research platform

Editor’s picks

Last updated: 21 December 2023

Last updated: 16 December 2023

Last updated: 6 October 2023

Last updated: 5 March 2024

Last updated: 25 November 2023

Last updated: 15 February 2024

Last updated: 11 March 2024

Last updated: 12 December 2023

Last updated: 6 March 2024

Last updated: 10 April 2023

Last updated: 20 December 2023

Latest articles

Related topics, log in or sign up.

Get started for free

Banner

Basic Research

  • Research Tips and Tricks
  • Finding the library
  • Finding a Book

Identify Research Methods in Articles

  • Cite Your Sources This link opens in a new window

When reviewing research resulting from your search, begin by reading the abstract. The abstract can help you identify the methodologies used in the study. Often research articles include a section that describes the methods used in more detail.  

Quantitative and qualitative methods are the two main categories of empirical research.  

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

  • << Previous: Finding a Book
  • Next: Cite Your Sources >>
  • Last Updated: Sep 5, 2023 2:42 PM
  • URL: https://libguides.umsl.edu/research

Enago Academy

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

' src=

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.

qualitative vs. quantitative research

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.

' src=

Well explained and easy to understand.

Rate this article Cancel Reply

Your email address will not be published.

research methodology quantitative and qualitative

Enago Academy's Most Popular Articles

Content Analysis vs Thematic Analysis: What's the difference?

  • Reporting Research

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

In research, choosing the right approach to understand data is crucial for deriving meaningful insights.…

Confounding Variables

Demystifying the Role of Confounding Variables in Research

In the realm of scientific research, the pursuit of knowledge often involves complex investigations, meticulous…

Research Interviews for Data Collection

Research Interviews: An effective and insightful way of data collection

Research interviews play a pivotal role in collecting data for various academic, scientific, and professional…

Planning Your Data Collection

Planning Your Data Collection: Designing methods for effective research

Planning your research is very important to obtain desirable results. In research, the relevance of…

research methodology quantitative and qualitative

  • Manuscripts & Grants
  • Trending Now

Unraveling Research Population and Sample: Understanding their role in statistical inference

Research population and sample serve as the cornerstones of any scientific inquiry. They hold the…

6 Steps to Evaluate the Effectiveness of Statistical Hypothesis Testing

How to Use Creative Data Visualization Techniques for Easy Comprehension of…

research methodology quantitative and qualitative

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

  • 2000+ blog articles
  • 50+ Webinars
  • 10+ Expert podcasts
  • 50+ Infographics
  • 10+ Checklists
  • Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

research methodology quantitative and qualitative

What should universities' stance be on AI tools in research and academic writing?

Banner Image

Quantitative and Qualitative Research

  • I NEED TO . . .
  • What is Quantitative Research?
  • What is Qualitative Research?
  • Quantitative vs Qualitative
  • Step 1: Accessing CINAHL
  • Step 2: Create a Keyword Search
  • Step 3: Create a Subject Heading Search
  • Step 4: Repeat Steps 1-3 for Second Concept
  • Step 5: Repeat Steps 1-3 for Quantitative Terms
  • Step 6: Combining All Searches
  • Step 7: Adding Limiters
  • Step 8: Save Your Search!
  • What Kind of Article is This?
  • More Research Help This link opens in a new window

What is qualitative research?

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

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

The following video will explain the fundamentals of qualitative research.

  • << Previous: What is Quantitative Research?
  • Next: Quantitative vs Qualitative >>
  • Last Updated: Dec 8, 2023 10:05 PM
  • URL: https://libguides.uta.edu/quantitative_and_qualitative_research

University of Texas Arlington Libraries 702 Planetarium Place · Arlington, TX 76019 · 817-272-3000

  • Internet Privacy
  • Accessibility
  • Problems with a guide? Contact Us.

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

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.

research methodology quantitative and qualitative

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.

Steps to Combine Q&A with Qualitative Research Methods

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.

Steps to Combine Q&A with Quantitative Research Methods

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. 

' src=

More from AhaSlides

7 Sample Likert Scale Questionnaires for Effective Research

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • Perspect Clin Res
  • v.14(1); Jan-Mar 2023
  • PMC10003579

Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

An external file that holds a picture, illustration, etc.
Object name is PCR-14-39-g001.jpg

Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

An external file that holds a picture, illustration, etc.
Object name is PCR-14-39-g002.jpg

Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

An external file that holds a picture, illustration, etc.
Object name is PCR-14-39-g003.jpg

Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Advertisement

Advertisement

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

Cite this article

  • 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  

2853 Accesses

11 Citations

9 Altmetric

Explore all metrics

A Correction to this article was published on 24 March 2022

This article has been updated

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

Source: Authors’ elaboration

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

research methodology quantitative and qualitative

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

Similar content being viewed by others

research methodology quantitative and qualitative

Common Methods and Sustainability Indicators

research methodology quantitative and qualitative

Transition heuristic frameworks in research on agro-food sustainability transitions

Hamid El Bilali

research methodology quantitative and qualitative

Research on agro-food sustainability transitions: where are food security and nutrition?

Change history, 24 march 2022.

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

Acero AP, Rodriguez C, Ciroth A (2017) LCIA methods: impact assessment methods in life cycle assessment and their impact categories. Version 1.5.6. Green Delta 1–23

Accorsi R, Versari L, Manzini R (2015) Glass vs. plastic: Life cycle assessment of extra-virgin olive oil bottles across global supply chains. Sustain 7:2818–2840. https://doi.org/10.3390/su7032818

Adjei-Bamfo P, Maloreh-Nyamekye T, Ahenkan A (2019) The role of e-government in sustainable public procurement in developing countries: a systematic literature review. Resour Conserv Recycl 142:189–203. https://doi.org/10.1016/j.resconrec.2018.12.001

Article   Google Scholar  

Aivazidou E, Tsolakis N, Vlachos D, Iakovou E (2015) Water footprint management policies for agrifood supply chains: a critical taxonomy and a system dynamics modelling approach. Chem Eng Trans 43:115–120. https://doi.org/10.3303/CET1543020

Alhaddi H (2015) Triple bottom line and sustainability: a literature review. Bus Manag Stud 1:6–10

Allaoui H, Guo Y, Sarkis J (2019) Decision support for collaboration planning in sustainable supply chains. J Clean Prod 229:761–774. https://doi.org/10.1016/j.jclepro.2019.04.367

Alshqaqeeq F, Amin Esmaeili M, Overcash M, Twomey J (2020) Quantifying hospital services by carbon footprint: a systematic literature review of patient care alternatives. Resour Conserv Recycl 154:104560. https://doi.org/10.1016/j.resconrec.2019.104560

Anwar F, Chaudhry FN, Nazeer S et al (2016) Causes of ozone layer depletion and its effects on human: review. Atmos Clim Sci 06:129–134. https://doi.org/10.4236/acs.2016.61011

Aquilani B, Silvestri C, Ruggieri A (2016). A Systematic Literature Review on Total Quality Management Critical Success Factors and the Identification of New Avenues of Research. https://doi.org/10.1108/TQM-01-2016-0003

Aramyan L, Hoste R, Van Den Broek W et al (2011) Towards sustainable food production: a scenario study of the European pork sector. J Chain Netw Sci 11:177–189. https://doi.org/10.3920/JCNS2011.Qpork8

Arfini F, Antonioli F, Cozzi E et al (2019) Sustainability, innovation and rural development: the case of Parmigiano-Reggiano PDO. Sustain 11:1–17. https://doi.org/10.3390/su11184978

Assembly UG (2005) Resolution adopted by the general assembly. New York, NY

Avilés-Palacios C, Rodríguez-Olalla A (2021) The sustainability of waste management models in circular economies. Sustain 13:1–19. https://doi.org/10.3390/su13137105

Azevedo SG, Silva ME, Matias JCO, Dias GP (2018) The influence of collaboration initiatives on the sustainability of the cashew supply chain. Sustain 10:1–29. https://doi.org/10.3390/su10062075

Bajaj S, Garg R, Sethi M (2016) Total quality management: a critical literature review using Pareto analysis. Int J Product Perform Manag 67:128–154

Banasik A, Kanellopoulos A, Bloemhof-Ruwaard JM, Claassen GDH (2019) Accounting for uncertainty in eco-efficient agri-food supply chains: a case study for mushroom production planning. J Clean Prod 216:249–256. https://doi.org/10.1016/j.jclepro.2019.01.153

Barth H, Ulvenblad PO, Ulvenblad P (2017) Towards a conceptual framework of sustainable business model innovation in the agri-food sector: a systematic literature review. Sustain 9. https://doi.org/10.3390/su9091620

Bastas A, Liyanage K (2018) Sustainable supply chain quality management: a systematic review

Beckerman W (1992) Economic growth and the environment: whose growth? Whose environment? World Dev 20:481–496. https://doi.org/10.1016/0305-750X(92)90038-W

Belaud JP, Prioux N, Vialle C, Sablayrolles C (2019) Big data for agri-food 4.0: application to sustainability management for by-products supply chain. Comput Ind 111:41–50. https://doi.org/10.1016/j.compind.2019.06.006

Bele B, Norderhaug A, Sickel H (2018) Localized agri-food systems and biodiversity. Agric 8. https://doi.org/10.3390/agriculture8020022

Bilali H El, Calabrese G, Iannetta M et al (2020) Environmental sustainability of typical agro-food products: a scientifically sound and user friendly approach. New Medit 19:69–83. https://doi.org/10.30682/nm2002e

Blanc S, Massaglia S, Brun F et al (2019) Use of bio-based plastics in the fruit supply chain: an integrated approach to assess environmental, economic, and social sustainability. Sustain 11. https://doi.org/10.3390/su11092475

Bloemhof JM, van der Vorst JGAJ, Bastl M, Allaoui H (2015) Sustainability assessment of food chain logistics. Int J Logist Res Appl 18:101–117. https://doi.org/10.1080/13675567.2015.1015508

Bonisoli L, Galdeano-Gómez E, Piedra-Muñoz L (2018) Deconstructing criteria and assessment tools to build agri-sustainability indicators and support farmers’ decision-making process. J Clean Prod 182:1080–1094. https://doi.org/10.1016/j.jclepro.2018.02.055

Bonisoli L, Galdeano-Gómez E, Piedra-Muñoz L, Pérez-Mesa JC (2019) Benchmarking agri-food sustainability certifications: evidences from applying SAFA in the Ecuadorian banana agri-system. J Clean Prod 236. https://doi.org/10.1016/j.jclepro.2019.07.054

Bornmann L, Haunschild R, Hug SE (2018) Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis. Scientometrics 114:427–437. https://doi.org/10.1007/s11192-017-2591-8

Boulding KE (1966) The economics of the coming spaceship earth. New York, 1-17

Bracquené E, Dewulf W, Duflou JR (2020) Measuring the performance of more circular complex product supply chains. Resour Conserv Recycl 154:104608. https://doi.org/10.1016/j.resconrec.2019.104608

Burck J, Hagen U, Bals C et al (2021) Climate Change Performance Index

Calisto Friant M, Vermeulen WJV, Salomone R (2020) A typology of circular economy discourses: navigating the diverse visions of a contested paradigm. Resour Conserv Recycl 161:104917. https://doi.org/10.1016/j.resconrec.2020.104917

Campbell BM, Beare DJ, Bennett EM et al (2017) Agriculture production as a major driver of the earth system exceeding planetary boundaries. Ecol Soc 22. https://doi.org/10.5751/ES-09595-220408

Capitanio F, Coppola A, Pascucci S (2010) Product and process innovation in the Italian food industry. Agribusiness 26:503–518. https://doi.org/10.1002/agr.20239

Caputo P, Zagarella F, Cusenza MA et al (2020) Energy-environmental assessment of the UIA-OpenAgri case study as urban regeneration project through agriculture. Sci Total Environ 729:138819. https://doi.org/10.1016/j.scitotenv.2020.138819

Article   CAS   Google Scholar  

Chabowski BR, Mena JA, Gonzalez-Padron TL (2011) The structure of sustainability research in marketing, 1958–2008: a basis for future research opportunities. J Acad Mark Sci 39:55–70. https://doi.org/10.1007/s11747-010-0212-7

Chadegani AA, Salehi H, Yunus M et al (2017) A comparison between two main academic literature collections : Web of Science and Scopus databases. Asian Soc Sci 9:18–26. https://doi.org/10.5539/ass.v9n5p18

Chams N, Guesmi B, Gil JM (2020) Beyond scientific contribution: assessment of the societal impact of research and innovation to build a sustainable agri-food sector. J Environ Manage 264. https://doi.org/10.1016/j.jenvman.2020.110455

Chandrakumar C, McLaren SJ, Jayamaha NP, Ramilan T (2019) Absolute sustainability-based life cycle assessment (ASLCA): a benchmarking approach to operate agri-food systems within the 2°C global carbon budget. J Ind Ecol 23:906–917. https://doi.org/10.1111/jiec.12830

Chaparro-Africano AM (2019) Toward generating sustainability indicators for agroecological markets. Agroecol Sustain Food Syst 43:40–66. https://doi.org/10.1080/21683565.2019.1566192

Colicchia C, Strozzi F (2012) Supply chain risk management: a new methodology for a systematic literature review

Conca L, Manta F, Morrone D, Toma P (2021) The impact of direct environmental, social, and governance reporting: empirical evidence in European-listed companies in the agri-food sector. Bus Strateg Environ 30:1080–1093. https://doi.org/10.1002/bse.2672

Coppola A, Ianuario S, Romano S, Viccaro M (2020) Corporate social responsibility in agri-food firms: the relationship between CSR actions and firm’s performance. AIMS Environ Sci 7:542–558. https://doi.org/10.3934/environsci.2020034

Corona B, Shen L, Reike D et al (2019) Towards sustainable development through the circular economy—a review and critical assessment on current circularity metrics. Resour Conserv Recycl 151:104498. https://doi.org/10.1016/j.resconrec.2019.104498

Correia MS (2019) Sustainability: An overview of the triple bottom line and sustainability implementation. Int J Strateg Eng 2:29–38.  https://doi.org/10.4018/IJoSE.2019010103

Coteur I, Marchand F, Debruyne L, Lauwers L (2019) Structuring the myriad of sustainability assessments in agri-food systems: a case in Flanders. J Clean Prod 209:472–480. https://doi.org/10.1016/j.jclepro.2018.10.066

CREA (2020) L’agricoltura italiana conta 2019

Crenna E, Sala S, Polce C, Collina E (2017) Pollinators in life cycle assessment: towards a framework for impact assessment. J Clean Prod 140:525–536. https://doi.org/10.1016/j.jclepro.2016.02.058

D’Eusanio M, Serreli M, Zamagni A, Petti L (2018) Assessment of social dimension of a jar of honey: a methodological outline. J Clean Prod 199:503–517. https://doi.org/10.1016/j.jclepro.2018.07.157

Dania WAP, Xing K, Amer Y (2018) Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review. J Clean Prod 186:851–864

De Pascale A, Arbolino R, Szopik-Depczyńska K et al (2021) A systematic review for measuring circular economy: the 61 indicators. J Clean Prod 281. https://doi.org/10.1016/j.jclepro.2020.124942

De Schoenmakere M, Gillabel J (2017) Circular by design: products in the circular economy

Del Borghi A, Gallo M, Strazza C, Del Borghi M (2014) An evaluation of environmental sustainability in the food industry through life cycle assessment: the case study of tomato products supply chain. J Clean Prod 78:121–130. https://doi.org/10.1016/j.jclepro.2014.04.083

Del Borghi A, Strazza C, Magrassi F et al (2018) Life cycle assessment for eco-design of product–package systems in the food industry—the case of legumes. Sustain Prod Consum 13:24–36. https://doi.org/10.1016/j.spc.2017.11.001

Denyer D, Tranfield D (2009) Producing a systematic review. In: Buchanan B (ed) The sage handbook of organization research methods. Sage Publications Ltd, Cornwall, pp 671–689

Google Scholar  

Dietz T, Grabs J, Chong AE (2019) Mainstreamed voluntary sustainability standards and their effectiveness: evidence from the Honduran coffee sector. Regul Gov. https://doi.org/10.1111/rego.12239

Dixon-Woods M (2011) Using framework-based synthesis for conducting reviews of qualitative studies. BMC Med 9:9–10. https://doi.org/10.1186/1741-7015-9-39

do Canto NR, Bossle MB, Marques L, Dutra M, (2020) Supply chain collaboration for sustainability: a qualitative investigation of food supply chains in Brazil. Manag Environ Qual an Int J. https://doi.org/10.1108/MEQ-12-2019-0275

dos Santos RR, Guarnieri P (2020) Social gains for artisanal agroindustrial producers induced by cooperation and collaboration in agri-food supply chain. Soc Responsib J. https://doi.org/10.1108/SRJ-09-2019-0323

Doukidis GI, Matopoulos A, Vlachopoulou M, Manthou V, Manos B (2007) A conceptual framework for supply chain collaboration: empirical evidence from the agri‐food industry. Supply Chain Manag an Int Journal 12:177–186. https://doi.org/10.1108/13598540710742491

Durach CF, Kembro J, Wieland A (2017) A new paradigm for systematic literature reviews in supply chain management. J Supply Chain Manag 53:67–85. https://doi.org/10.1111/jscm.12145

Durán-Sánchez A, Álvarez-García J, Río-Rama D, De la Cruz M (2018) Sustainable water resources management: a bibliometric overview. Water 10:1–19. https://doi.org/10.3390/w10091191

Duru M, Therond O (2015) Livestock system sustainability and resilience in intensive production zones: which form of ecological modernization? Reg Environ Chang 15:1651–1665. https://doi.org/10.1007/s10113-014-0722-9

Edison Fondazione (2019) Le eccellenze agricole italiane. I primati europei e mondiali dell’Italia nei prodotti vegetali. Milan (IT)

Ehrenfeld JR (2005) The roots of sustainability. MIT Sloan Manag Rev 46(2)46:23–25

Elia V, Gnoni MG, Tornese F (2017) Measuring circular economy strategies through index methods: a critical analysis. J Clean Prod 142:2741–2751. https://doi.org/10.1016/j.jclepro.2016.10.196

Elkington J (1997) Cannibals with forks : the triple bottom line of 21st century business. Capstone, Oxford

Esposito B, Sessa MR, Sica D, Malandrino O (2020) Towards circular economy in the agri-food sector. A systematic literature review. Sustain 12. https://doi.org/10.3390/SU12187401

European Commission (2018) Agri-food trade in 2018

European Commission (2019) Monitoring EU agri-food trade: development until September 2019

Eurostat (2018) Small and large farms in the EU - statistics from the farm structure survey

FAO (2011) Biodiversity for food and agriculture. Italy, Rome

FAO (2012) Energy-smart food at FAO: an overview. Italy, Rome

FAO (2014) Food wastage footprint: fool cost-accounting

FAO (2016) The state of food and agriculture climate change, agriculture and food security. Italy, Rome

FAO (2017) The future of food and agriculture: trends and challenges. Italy, Rome

FAO (2020) The state of food security and nutrition in the world. Transforming Food Systems for Affordable Healthy Diets. Rome, Italy

Fassio F, Tecco N (2019) Circular economy for food: a systemic interpretation of 40 case histories in the food system in their relationships with SDGs. Systems 7:43. https://doi.org/10.3390/systems7030043

Fathollahi A, Coupe SJ (2021) Life cycle assessment (LCA) and life cycle costing (LCC) of road drainage systems for sustainability evaluation: quantifying the contribution of different life cycle phases. Sci Total Environ 776:145937. https://doi.org/10.1016/j.scitotenv.2021.145937

Ferreira VJ, Arnal ÁJ, Royo P et al (2019) Energy and resource efficiency of electroporation-assisted extraction as an emerging technology towards a sustainable bio-economy in the agri-food sector. J Clean Prod 233:1123–1132. https://doi.org/10.1016/j.jclepro.2019.06.030

Fiksel J (2006) A framework for sustainable remediation. JOM 8:15–22. https://doi.org/10.1021/es202595w

Flick U (2014) An introduction to qualitative research

Franciosi C, Voisin A, Miranda S et al (2020) Measuring maintenance impacts on sustainability of manufacturing industries : from a systematic literature review to a framework proposal. J Clean Prod 260:1–19. https://doi.org/10.1016/j.jclepro.2020.121065

Gaitán-Cremaschi D, Meuwissen MPM, Oude AGJML (2017) Total factor productivity: a framework for measuring agri-food supply chain performance towards sustainability. Appl Econ Perspect Policy 39:259–285. https://doi.org/10.1093/aepp/ppw008

Galdeano-Gómez E, Zepeda-Zepeda JA, Piedra-Muñoz L, Vega-López LL (2017) Family farm’s features influencing socio-economic sustainability: an analysis of the agri-food sector in southeast Spain. New Medit 16:50–61

Gallopín G, Herrero LMJ, Rocuts A (2014) Conceptual frameworks and visual interpretations of sustainability. Int J Sustain Dev 17:298–326. https://doi.org/10.1504/IJSD.2014.064183

Gallopín GC (2003) Sostenibilidad y desarrollo sostenible: un enfoque sistémico. Cepal, LATIN AMERICA

Garnett T (2013) Food sustainability: problems, perspectives and solutions. Proc Nutr Soc 72:29–39. https://doi.org/10.1017/S0029665112002947

Garofalo P, D’Andrea L, Tomaiuolo M et al (2017) Environmental sustainability of agri-food supply chains in Italy: the case of the whole-peeled tomato production under life cycle assessment methodology. J Food Eng 200:1–12. https://doi.org/10.1016/j.jfoodeng.2016.12.007

Gava O, Bartolini F, Venturi F et al (2018) A reflection of the use of the life cycle assessment tool for agri-food sustainability. Sustain 11. https://doi.org/10.3390/su11010071

Gazzola P, Querci E (2017) The connection between the quality of life and sustainable ecological development. Eur Sci J 7881:1857–7431

Geissdoerfer M, Savaget P, Bocken N, Hultink EJ (2017) The circular economy – a new sustainability paradigm ? The circular economy – a new sustainability paradigm ? J Clean Prod 143:757–768. https://doi.org/10.1016/j.jclepro.2016.12.048

Georgescu-Roegen N (1971) The entropy low and the economic process. Harward University Press, Cambridge Mass

Book   Google Scholar  

Gerbens-Leenes PW, Moll HC, Schoot Uiterkamp AJM (2003) Design and development of a measuring method for environmental sustainability in food production systems. Ecol Econ 46:231–248. https://doi.org/10.1016/S0921-8009(03)00140-X

Gésan-Guiziou G, Alaphilippe A, Aubin J et al (2020) Diversity and potentiality of multi-criteria decision analysis methods for agri-food research. Agron Sustain Dev 40. https://doi.org/10.1007/s13593-020-00650-3

Ghisellini P, Cialani C, Ulgiati S (2016) A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems. J Clean Prod 114:11–32. https://doi.org/10.1016/j.jclepro.2015.09.007

Godoy-Durán Á, Galdeano- Gómez E, Pérez-Mesa JC, Piedra-Muñoz L (2017) Assessing eco-efficiency and the determinants of horticultural family-farming in southeast Spain. J Environ Manage 204:594–604. https://doi.org/10.1016/j.jenvman.2017.09.037

Gold S, Kunz N, Reiner G (2017) Sustainable global agrifood supply chains: exploring the barriers. J Ind Ecol 21:249–260. https://doi.org/10.1111/jiec.12440

Goucher L, Bruce R, Cameron DD et al (2017) The environmental impact of fertilizer embodied in a wheat-to-bread supply chain. Nat Plants 3:1–5. https://doi.org/10.1038/nplants.2017.12

Green A, Nemecek T, Chaudhary A, Mathys A (2020) Assessing nutritional, health, and environmental sustainability dimensions of agri-food production. Glob Food Sec 26:100406. https://doi.org/10.1016/j.gfs.2020.100406

Guinée JB, Heijungs R, Huppes G et al (2011) Life cycle assessment: past, present, and future †. Environ Sci Technol 45:90–96. https://doi.org/10.1021/es101316v

Guiomar N, Godinho S, Pinto-Correia T et al (2018) Typology and distribution of small farms in Europe: towards a better picture. Land Use Policy 75:784–798. https://doi.org/10.1016/j.landusepol.2018.04.012

Gunasekaran A, Patel C, McGaughey RE (2004) A framework for supply chain performance measurement. Int J Prod Econ 87:333–347. https://doi.org/10.1016/j.ijpe.2003.08.003

Gunasekaran A, Patel C, Tirtiroglu E (2001) Performance measures and metrics in a supply chain environment. Int J Oper Prod Manag 21:71–87. https://doi.org/10.1108/01443570110358468

Hamam M, Chinnici G, Di Vita G et al (2021) Circular economy models in agro-food systems: a review. Sustain 13

Harun SN, Hanafiah MM, Aziz NIHA (2021) An LCA-based environmental performance of rice production for developing a sustainable agri-food system in Malaysia. Environ Manage 67:146–161. https://doi.org/10.1007/s00267-020-01365-7

Harvey M, Pilgrim S (2011) The new competition for land: food, energy, and climate change. Food Policy 36:S40–S51. https://doi.org/10.1016/j.foodpol.2010.11.009

Hawkes C, Ruel MT (2006) Understanding the links between agriculture and health. DC: International Food Policy Research Institute. Washington, USA

Hellweg S, Milà i Canals L (2014) Emerging approaches, challenges and opportunities in life cycle assessment. Science (80)344:1109LP–1113. https://doi.org/10.1126/science.1248361

Higgins V, Dibden J, Cocklin C (2015) Private agri-food governance and greenhouse gas abatement: constructing a corporate carbon economy. Geoforum 66:75–84. https://doi.org/10.1016/j.geoforum.2015.09.012

Hill T (1995) Manufacturing strategy: text and cases., Macmillan

Hjeresen DD, Gonzales R (2020) Green chemistry promote sustainable agriculture?The rewards are higher yields and less environmental contamination. Environemental Sci Techonology 103–107

Horne R, Grant T, Verghese K (2009) Life cycle assessment: principles, practice, and prospects. Csiro Publishing, Collingwood, Australia

Horton P, Koh L, Guang VS (2016) An integrated theoretical framework to enhance resource efficiency, sustainability and human health in agri-food systems. J Clean Prod 120:164–169. https://doi.org/10.1016/j.jclepro.2015.08.092

Hospido A, Davis J, Berlin J, Sonesson U (2010) A review of methodological issues affecting LCA of novel food products. Int J Life Cycle Assess 15:44–52. https://doi.org/10.1007/s11367-009-0130-4

Huffman T, Liu J, Green M et al (2015) Improving and evaluating the soil cover indicator for agricultural land in Canada. Ecol Indic 48:272–281. https://doi.org/10.1016/j.ecolind.2014.07.008

Ilbery B, Maye D (2005) Food supply chains and sustainability: evidence from specialist food producers in the Scottish/English borders. Land Use Policy 22:331–344. https://doi.org/10.1016/j.landusepol.2004.06.002

Ingrao C, Faccilongo N, Valenti F et al (2019) Tomato puree in the Mediterranean region: an environmental life cycle assessment, based upon data surveyed at the supply chain level. J Clean Prod 233:292–313. https://doi.org/10.1016/j.jclepro.2019.06.056

Iocola I, Angevin F, Bockstaller C et al (2020) An actor-oriented multi-criteria assessment framework to support a transition towards sustainable agricultural systems based on crop diversification. Sustain 12. https://doi.org/10.3390/su12135434

Irabien A, Darton RC (2016) Energy–water–food nexus in the Spanish greenhouse tomato production. Clean Technol Environ Policy 18:1307–1316. https://doi.org/10.1007/s10098-015-1076-9

ISO 14040:2006 (2006) Environmental management — life cycle assessment — principles and framework

ISO 14044:2006 (2006) Environmental management — life cycle assessment — requirements and guidelines

ISO 15392:2008 (2008) Sustainability in building construction–general principles

Istat (2019) Andamento dell’economia agricola

Jaakkola E (2020) Designing conceptual articles : four approaches. AMS Rev 1–9. https://doi.org/10.1007/s13162-020-00161-0

Jin R, Yuan H, Chen Q (2019) Science mapping approach to assisting the review of construction and demolition waste management research published between 2009 and 2018. Resour Conserv Recycl 140:175–188. https://doi.org/10.1016/j.resconrec.2018.09.029

Johnston P, Everard M, Santillo D, Robèrt KH (2007) Reclaiming the definition of sustainability. Environ Sci Pollut Res Int 14:60–66. https://doi.org/10.1065/espr2007.01.375

Jorgensen SE, Burkhard B, Müller F (2013) Twenty volumes of ecological indicators-an accounting short review. Ecol Indic 28:4–9. https://doi.org/10.1016/j.ecolind.2012.12.018

Joshi S, Sharma M, Kler R (2020) Modeling circular economy dimensions in agri-tourism clusters: sustainable performance and future research directions. Int J Math Eng Manag Sci 5:1046–1061. https://doi.org/10.33889/IJMEMS.2020.5.6.080

Kamilaris A, Gao F, Prenafeta-Boldu FX, Ali MI (2017) Agri-IoT: a semantic framework for Internet of Things-enabled smart farming applications. In: 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016. pp 442–447

Karuppusami G, Gandhinathan R (2006) Pareto analysis of critical success factors of total quality management: a literature review and analysis. TQM Mag 18:372–385. https://doi.org/10.1108/09544780610671048

Kates RW, Parris TM, Leiserowitz AA (2005) What is sustainable development? Goals, indicators, values, and practice. Environ Sci Policy Sustain Dev 47:8–21. https://doi.org/10.1080/00139157.2005.10524444

Khounani Z, Hosseinzadeh-Bandbafha H, Moustakas K et al (2021) Environmental life cycle assessment of different biorefinery platforms valorizing olive wastes to biofuel, phosphate salts, natural antioxidant, and an oxygenated fuel additive (triacetin). J Clean Prod 278:123916. https://doi.org/10.1016/j.jclepro.2020.123916

Kitchenham B, Charters S (2007) Guidelines for performing systematic literature reviews in software engineering version 2.3. Engineering 45. https://doi.org/10.1145/1134285.1134500

Korhonen J, Nuur C, Feldmann A, Birkie SE (2018) Circular economy as an essentially contested concept. J Clean Prod 175:544–552. https://doi.org/10.1016/j.jclepro.2017.12.111

Kuisma M, Kahiluoto H (2017) Biotic resource loss beyond food waste: agriculture leaks worst. Resour Conserv Recycl 124:129–140. https://doi.org/10.1016/j.resconrec.2017.04.008

Laso J, Hoehn D, Margallo M et al (2018) Assessing energy and environmental efficiency of the Spanish agri-food system using the LCA/DEA methodology. Energies 11. https://doi.org/10.3390/en11123395

Lee KM (2007) So What is the “triple bottom line”? Int J Divers Organ Communities Nations Annu Rev 6:67–72. https://doi.org/10.18848/1447-9532/cgp/v06i06/39283

Lehmann RJ, Hermansen JE, Fritz M et al (2011) Information services for European pork chains - closing gaps in information infrastructures. Comput Electron Agric 79:125–136. https://doi.org/10.1016/j.compag.2011.09.002

León-Bravo V, Caniato F, Caridi M, Johnsen T (2017) Collaboration for sustainability in the food supply chain: a multi-stage study in Italy. Sustainability 9:1253

Lepage A (2009) The quality of life as attribute of sustainability. TQM J 21:105–115. https://doi.org/10.1108/17542730910938119

Li CZ, Zhao Y, Xiao B et al (2020) Research trend of the application of information technologies in construction and demolition waste management. J Clean Prod 263. https://doi.org/10.1016/j.jclepro.2020.121458

Lo Giudice A, Mbohwa C, Clasadonte MT, Ingrao C (2014) Life cycle assessment interpretation and improvement of the Sicilian artichokes production. Int J Environ Res 8:305–316. https://doi.org/10.22059/ijer.2014.721

Lueddeckens S, Saling P, Guenther E (2020) Temporal issues in life cycle assessment—a systematic review. Int J Life Cycle Assess 25:1385–1401. https://doi.org/10.1007/s11367-020-01757-1

Luo J, Ji C, Qiu C, Jia F (2018) Agri-food supply chain management: bibliometric and content analyses. Sustain 10. https://doi.org/10.3390/su10051573

Lynch J, Donnellan T, Finn JA et al (2019) Potential development of Irish agricultural sustainability indicators for current and future policy evaluation needs. J Environ Manage 230:434–445. https://doi.org/10.1016/j.jenvman.2018.09.070

MacArthur E (2013) Towards the circular economy. J Ind Ecol 2:23–44

MacArthur E (2017) Delivering the circular economy a toolkit for policymakers, The Ellen MacArthur Foundation

MacInnis DJ (2011) A framework for conceptual. J Mark 75:136–154. https://doi.org/10.1509/jmkg.75.4.136

Mangla SK, Luthra S, Rich N et al (2018) Enablers to implement sustainable initiatives in agri-food supply chains. Int J Prod Econ 203:379–393. https://doi.org/10.1016/j.ijpe.2018.07.012

Marotta G, Nazzaro C, Stanco M (2017) How the social responsibility creates value: models of innovation in Italian pasta industry. Int J Glob Small Bus 9:144–167. https://doi.org/10.1504/IJGSB.2017.088923

Martucci O, Arcese G, Montauti C, Acampora A (2019) Social aspects in the wine sector: comparison between social life cycle assessment and VIVA sustainable wine project indicators. Resources 8. https://doi.org/10.3390/resources8020069

Mayring P (2004) Forum : Qualitative social research Sozialforschung 2. History of content analysis. A Companion to Qual Res 1:159–176

McKelvey B (2002) Managing coevolutionary dynamics. In: 18th EGOS Conference. Barcelona, Spain, pp 1–21

McMichael AJ, Butler CD, Folke C (2003) New visions for addressing sustainability. Science (80- ) 302:1191–1920

Mehmood A, Ahmed S, Viza E et al (2021) Drivers and barriers towards circular economy in agri-food supply chain: a review. Bus Strateg Dev 1–17. https://doi.org/10.1002/bsd2.171

Mella P, Gazzola P (2011) Sustainability and quality of life: the development model. In: Kapounek S (ed) Enterprise and competitive environment. Mendel University: Brno, Czechia. 542–551

Merli R, Preziosi M, Acampora A (2018) How do scholars approach the circular economy ? A systematic literature review. J Clean Prod 178:703–722. https://doi.org/10.1016/j.jclepro.2017.12.112

Merli R, Preziosi M, Acampora A et al (2020) Recycled fibers in reinforced concrete: a systematic literature review. J Clean Prod 248:119207. https://doi.org/10.1016/j.jclepro.2019.119207

Miglietta PP, Morrone D (2018) Managing water sustainability: virtual water flows and economic water productivity assessment of the wine trade between Italy and the Balkans. Sustain 10. https://doi.org/10.3390/su10020543

Mitchell MGE, Chan KMA, Newlands NK, Ramankutty N (2020) Spatial correlations don’t predict changes in agricultural ecosystem services: a Canada-wide case study. Front Sustain Food Syst 4:1–17. https://doi.org/10.3389/fsufs.2020.539892

Moraga G, Huysveld S, Mathieux F et al (2019) Circular economy indicators: what do they measure?. Resour Conserv Recycl 146:452–461. https://doi.org/10.1016/j.resconrec.2019.03.045

Morrissey JE, Dunphy NP (2015) Towards sustainable agri-food systems: the role of integrated sustainability and value assessment across the supply-chain. Int J Soc Ecol Sustain Dev 6:41–58. https://doi.org/10.4018/IJSESD.2015070104

Moser G (2009) Quality of life and sustainability: toward person-environment congruity. J Environ Psychol 29:351–357. https://doi.org/10.1016/j.jenvp.2009.02.002

Muijs D (2010) Doing quantitative research in education with SPSS. London

Muller MF, Esmanioto F, Huber N, Loures ER (2019) A systematic literature review of interoperability in the green Building Information Modeling lifecycle. J Clean Prod 223:397–412. https://doi.org/10.1016/j.jclepro.2019.03.114

Muradin M, Joachimiak-Lechman K, Foltynowicz Z (2018) Evaluation of eco-efficiency of two alternative agricultural biogas plants. Appl Sci 8. https://doi.org/10.3390/app8112083

Naseer MA, ur R, Ashfaq M, Hassan S, et al (2019) Critical issues at the upstream level in sustainable supply chain management of agri-food industries: evidence from Pakistan’s citrus industry. Sustain 11:1–19. https://doi.org/10.3390/su11051326

Nattassha R, Handayati Y, Simatupang TM, Siallagan M (2020) Understanding circular economy implementation in the agri-food supply chain: the case of an Indonesian organic fertiliser producer. Agric Food Secur 9:1–16. https://doi.org/10.1186/s40066-020-00264-8

Nazari-Sharabian M, Ahmad S, Karakouzian M (2018) Climate change and eutrophication: a short review. Eng Technol Appl Sci Res 8:3668–3672. https://doi.org/10.5281/zenodo.2532694

Nazir N (2017) Understanding life cycle thinking and its practical application to agri-food system. Int J Adv Sci Eng Inf Technol 7:1861–1870. https://doi.org/10.18517/ijaseit.7.5.3578

Negra C, Remans R, Attwood S et al (2020) Sustainable agri-food investments require multi-sector co-development of decision tools. Ecol Indic 110:105851. https://doi.org/10.1016/j.ecolind.2019.105851

Newsham KK, Robinson SA (2009) Responses of plants in polar regions to UVB exposure: a meta-analysis. Glob Chang Biol 15:2574–2589. https://doi.org/10.1111/j.1365-2486.2009.01944.x

Niemeijer D, de Groot RS (2008) A conceptual framework for selecting environmental indicator sets. Ecol Indic 8:14–25. https://doi.org/10.1016/j.ecolind.2006.11.012

Niero M, Kalbar PP (2019) Coupling material circularity indicators and life cycle based indicators: a proposal to advance the assessment of circular economy strategies at the product level. Resour Conserv Recycl 140:305–312. https://doi.org/10.1016/j.resconrec.2018.10.002

Nikolaou IE, Tsagarakis KP (2021) An introduction to circular economy and sustainability: some existing lessons and future directions. Sustain Prod Consum 28:600–609. https://doi.org/10.1016/j.spc.2021.06.017

Notarnicola B, Hayashi K, Curran MA, Huisingh D (2012) Progress in working towards a more sustainable agri-food industry. J Clean Prod 28:1–8. https://doi.org/10.1016/j.jclepro.2012.02.007

Notarnicola B, Tassielli G, Renzulli PA, Monforti F (2017) Energy flows and greenhouses gases of EU (European Union) national breads using an LCA (life cycle assessment) approach. J Clean Prod 140:455–469. https://doi.org/10.1016/j.jclepro.2016.05.150

Opferkuch K, Caeiro S, Salomone R, Ramos TB (2021) Circular economy in corporate sustainability reporting: a review of organisational approaches. Bus Strateg Environ 1–22. https://doi.org/10.1002/bse.2854

Padilla-Rivera A, do Carmo BBT, Arcese G, Merveille N, (2021) Social circular economy indicators: selection through fuzzy delphi method. Sustain Prod Consum 26:101–110. https://doi.org/10.1016/j.spc.2020.09.015

Pagotto M, Halog A (2016) Towards a circular economy in Australian agri-food industry: an application of input-output oriented approaches for analyzing resource efficiency and competitiveness potential. J Ind Ecol 20:1176–1186. https://doi.org/10.1111/jiec.12373

Parent G, Lavallée S (2011) LCA potentials and limits within a sustainable agri-food statutory framework. Global food insecurity. Springer, Netherlands, Dordrecht, pp 161–171

Chapter   Google Scholar  

Pattey E, Qiu G (2012) Trends in primary particulate matter emissions from Canadian agriculture. J Air Waste Manag Assoc 62:737–747. https://doi.org/10.1080/10962247.2012.672058

Pauliuk S (2018) Critical appraisal of the circular economy standard BS 8001:2017 and a dashboard of quantitative system indicators for its implementation in organizations. Resour Conserv Recycl 129:81–92. https://doi.org/10.1016/j.resconrec.2017.10.019

Peano C, Migliorini P, Sottile F (2014) A methodology for the sustainability assessment of agri-food systems: an application to the slow food presidia project. Ecol Soc 19. https://doi.org/10.5751/ES-06972-190424

Peano C, Tecco N, Dansero E et al (2015) Evaluating the sustainability in complex agri-food systems: the SAEMETH framework. Sustain 7:6721–6741. https://doi.org/10.3390/su7066721

Pearce DW, Turner RK (1990) Economics of natural resources and the environment. Harvester Wheatsheaf, Hemel Hempstead, Herts

Pelletier N (2018) Social sustainability assessment of Canadian egg production facilities: methods, analysis, and recommendations. Sustain 10:1–17. https://doi.org/10.3390/su10051601

Peña C, Civit B, Gallego-Schmid A et al (2021) Using life cycle assessment to achieve a circular economy. Int J Life Cycle Assess 26:215–220. https://doi.org/10.1007/s11367-020-01856-z

Perez Neira D (2016) Energy sustainability of Ecuadorian cacao export and its contribution to climate change. A case study through product life cycle assessment. J Clean Prod 112:2560–2568. https://doi.org/10.1016/j.jclepro.2015.11.003

Pérez-Neira D, Grollmus-Venegas A (2018) Life-cycle energy assessment and carbon footprint of peri-urban horticulture. A comparative case study of local food systems in Spain. Landsc Urban Plan 172:60–68. https://doi.org/10.1016/j.landurbplan.2018.01.001

Pérez-Pons ME, Plaza-Hernández M, Alonso RS et al (2021) Increasing profitability and monitoring environmental performance: a case study in the agri-food industry through an edge-iot platform. Sustain 13:1–16. https://doi.org/10.3390/su13010283

Petti L, Serreli M, Di Cesare S (2018) Systematic literature review in social life cycle assessment. Int J Life Cycle Assess 23:422–431. https://doi.org/10.1007/s11367-016-1135-4

Pieroni MPP, McAloone TC, Pigosso DCA (2019) Business model innovation for circular economy and sustainability: a review of approaches. J Clean Prod 215:198–216. https://doi.org/10.1016/j.jclepro.2019.01.036

Polit DF, Beck CT (2004) Nursing research: principles and methods. Lippincott Williams & Wilkins, Philadelphia, PA

Porkka M, Gerten D, Schaphoff S et al (2016) Causes and trends of water scarcity in food production. Environ Res Lett 11:015001. https://doi.org/10.1088/1748-9326/11/1/015001

Prajapati H, Kant R, Shankar R (2019) Bequeath life to death: state-of-art review on reverse logistics. J Clean Prod 211:503–520. https://doi.org/10.1016/j.jclepro.2018.11.187

Priyadarshini P, Abhilash PC (2020) Policy recommendations for enabling transition towards sustainable agriculture in India. Land Use Policy 96:104718. https://doi.org/10.1016/j.landusepol.2020.104718

Pronti A, Coccia M (2020) Multicriteria analysis of the sustainability performance between agroecological and conventional coffee farms in the East Region of Minas Gerais (Brazil). Renew Agric Food Syst. https://doi.org/10.1017/S1742170520000332

Rabadán A, González-Moreno A, Sáez-Martínez FJ (2019) Improving firms’ performance and sustainability: the case of eco-innovation in the agri-food industry. Sustain 11. https://doi.org/10.3390/su11205590

Raut RD, Luthra S, Narkhede BE et al (2019) Examining the performance oriented indicators for implementing green management practices in the Indian agro sector. J Clean Prod 215:926–943. https://doi.org/10.1016/j.jclepro.2019.01.139

Recanati F, Marveggio D, Dotelli G (2018) From beans to bar: a life cycle assessment towards sustainable chocolate supply chain. Sci Total Environ 613–614:1013–1023. https://doi.org/10.1016/j.scitotenv.2017.09.187

Redclift M (2005) Sustainable development (1987–2005): an oxymoron comes of age. Sustain Dev 13:212–227. https://doi.org/10.1002/sd.281

Rezaei M, Soheilifard F, Keshvari A (2021) Impact of agrochemical emission models on the environmental assessment of paddy rice production using life cycle assessment approach. Energy Sources. Part A Recover Util Environ Eff 1–16

Rigamonti L, Mancini E (2021) Life cycle assessment and circularity indicators. Int J Life Cycle Assess. https://doi.org/10.1007/s11367-021-01966-2

Risku-Norja H, Mäenpää I (2007) MFA model to assess economic and environmental consequences of food production and consumption. Ecol Econ 60:700–711. https://doi.org/10.1016/j.ecolecon.2006.05.001

Ritzén S, Sandström GÖ (2017) Barriers to the circular economy – integration of perspectives and domains. Procedia CIRP 64:7–12. https://doi.org/10.1016/j.procir.2017.03.005

Rockström J, Steffen W, Noone K et al (2009) A safe operating space for humanity. Nature 461:472–475. https://doi.org/10.1038/461472a

Roos Lindgreen E, Mondello G, Salomone R et al (2021) Exploring the effectiveness of grey literature indicators and life cycle assessment in assessing circular economy at the micro level: a comparative analysis. Int J Life Cycle Assess. https://doi.org/10.1007/s11367-021-01972-4

Roselli L, Casieri A, De Gennaro BC et al (2020) Environmental and economic sustainability of table grape production in Italy. Sustain 12.  https://doi.org/10.3390/su12093670

Ross RB, Pandey V, Ross KL (2015) Sustainability and strategy in U.S. agri-food firms: an assessment of current practices. Int Food Agribus Manag Rev 18:17–48

Royo P, Ferreira VJ, López-Sabirón AM, Ferreira G. (2016) Hybrid diagnosis to characterise the energy and environmental enhancement of photovoltaic modules using smart materials. Energy 101:174–189. https://doi.org/10.1016/j.energy.2016.01.101

Ruggerio CA (2021) Sustainability and sustainable development: a review of principles and definitions. Sci Total Environ 786:147481. https://doi.org/10.1016/j.scitotenv.2021.147481

Ruiz-Almeida A, Rivera-Ferre MG (2019) Internationally-based indicators to measure agri-food systems sustainability using food sovereignty as a conceptual framework. Food Secur 11:1321–1337. https://doi.org/10.1007/s12571-019-00964-5

Ryan M, Hennessy T, Buckley C et al (2016) Developing farm-level sustainability indicators for Ireland using the Teagasc National Farm Survey. Irish J Agric Food Res 55:112–125. https://doi.org/10.1515/ijafr-2016-0011

Saade MRM, Yahia A, Amor B (2020) How has LCA been applied to 3D printing ? A systematic literature review and recommendations for future studies. J Clean Prod 244:118803. https://doi.org/10.1016/j.jclepro.2019.118803

Saitone TL, Sexton RJ (2017) Agri-food supply chain: evolution and performance with conflicting consumer and societal demands. Eur Rev Agric Econ 44:634–657. https://doi.org/10.1093/erae/jbx003

Salim N, Ab Rahman MN, Abd Wahab D (2019) A systematic literature review of internal capabilities for enhancing eco-innovation performance of manufacturing firms. J Clean Prod 209:1445–1460. https://doi.org/10.1016/j.jclepro.2018.11.105

Salimi N (2021) Circular economy in agri-food systems BT - strategic decision making for sustainable management of industrial networks. In: International S (ed) Rezaei J. Publishing, Cham, pp 57–70

Salomone R, Ioppolo G (2012) Environmental impacts of olive oil production: a life cycle assessment case study in the province of Messina (Sicily). J Clean Prod 28:88–100. https://doi.org/10.1016/j.jclepro.2011.10.004

Sánchez AD, Río DMDLC, García JÁ (2017) Bibliometric analysis of publications on wine tourism in the databases Scopus and WoS. Eur Res Manag Bus Econ 23:8–15. https://doi.org/10.1016/j.iedeen.2016.02.001

Saputri VHL, Sutopo W, Hisjam M, Ma’aram A (2019) Sustainable agri-food supply chain performance measurement model for GMO and non-GMO using data envelopment analysis method. Appl Sci 9. https://doi.org/10.3390/app9061199

Sassanelli C, Rosa P, Rocca R, Terzi S (2019) Circular economy performance assessment methods : a systematic literature review. J Clean Prod 229:440–453. https://doi.org/10.1016/j.jclepro.2019.05.019

Schiefer S, Gonzalez C, Flanigan S (2015) More than just a factor in transition processes? The role of collaboration in agriculture. In: Sutherland LA, Darnhofer I, Wilson GA, Zagata L (eds) Transition pathways towards sustainability in agriculture: case studies from Europe, CPI Group. Croydon, UK, pp. 83

Seuring S, Muller M (2008) From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod 16:1699–1710. https://doi.org/10.1016/j.jclepro.2008.04.020

Silvestri C, Silvestri L, Forcina A, et al (2021) Green chemistry contribution towards more equitable global sustainability and greater circular economy: A systematic literature review. J Clean Prod 294. https://doi.org/10.1016/j.jclepro.2021.126137

Smetana S, Schmitt E, Mathys A (2019) Sustainable use of Hermetia illucens insect biomass for feed and food: attributional and consequential life cycle assessment. Resour Conserv Recycl 144:285–296. https://doi.org/10.1016/j.resconrec.2019.01.042

Sonesson U, Berlin J, Ziegler F (2010) Environmental assessment and management in the food industry: life cycle assessment and related approaches. Woodhead Publishing, Cambridge

Soussana JF (2014) Research priorities for sustainable agri-food systems and life cycle assessment. J Clean Prod 73:19–23. https://doi.org/10.1016/j.jclepro.2014.02.061

Soylu A, Oruç C, Turkay M et al (2006) Synergy analysis of collaborative supply chain management in energy systems using multi-period MILP. Eur J Oper Res 174:387–403. https://doi.org/10.1016/j.ejor.2005.02.042

Spaiser V, Ranganathan S, Swain RB, Sumpter DJ (2017) The sustainable development oxymoron: quantifying and modelling the incompatibility of sustainable development goals. Int J Sustain Dev World Ecol 24:457–470. https://doi.org/10.1080/13504509.2016.1235624

Stewart R, Niero M (2018) Circular economy in corporate sustainability strategies: a review of corporate sustainability reports in the fast-moving consumer goods sector. Bus Strateg Environ 27:1005–1022. https://doi.org/10.1002/bse.2048

Stillitano T, Spada E, Iofrida N et al (2021) Sustainable agri-food processes and circular economy pathways in a life cycle perspective: state of the art of applicative research. Sustain 13:1–29. https://doi.org/10.3390/su13052472

Stone J, Rahimifard S (2018) Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework. Supply Chain Manag 23:207–238. https://doi.org/10.1108/SCM-06-2017-0201

Strazza C, Del Borghi A, Gallo M, Del Borghi M (2011) Resource productivity enhancement as means for promoting cleaner production: analysis of co-incineration in cement plants through a life cycle approach. J Clean Prod 19:1615–1621. https://doi.org/10.1016/j.jclepro.2011.05.014

Su B, Heshmati A, Geng Y, Yu X (2013) A review of the circular economy in China: moving from rhetoric to implementation. J Clean Prod 42:215–227. https://doi.org/10.1016/j.jclepro.2012.11.020

Suárez-Eiroa B, Fernández E, Méndez-Martínez G, Soto-Oñate D (2019) Operational principles of circular economy for sustainable development: linking theory and practice. J Clean Prod 214:952–961. https://doi.org/10.1016/j.jclepro.2018.12.271

Svensson G, Wagner B (2015) Implementing and managing economic, social and environmental efforts of business sustainability. Manag Environ Qual an Int Journal 26:195–213. https://doi.org/10.1108/MEQ-09-2013-0099

Tasca AL, Nessi S, Rigamonti L (2017) Environmental sustainability of agri-food supply chains: an LCA comparison between two alternative forms of production and distribution of endive in northern Italy. J Clean Prod 140:725–741. https://doi.org/10.1016/j.jclepro.2016.06.170

Tassielli G, Notarnicola B, Renzulli PA, Arcese G (2018) Environmental life cycle assessment of fresh and processed sweet cherries in southern Italy. J Clean Prod 171:184–197. https://doi.org/10.1016/j.jclepro.2017.09.227

Teixeira R, Pax S (2011) A survey of life cycle assessment practitioners with a focus on the agri-food sector. J Ind Ecol 15:817–820. https://doi.org/10.1111/j.1530-9290.2011.00421.x

Tobergte DR, Curtis S (2013) ILCD Handbook. J Chem Info Model. https://doi.org/10.278/33030

Tortorella MM, Di Leo S, Cosmi C et al (2020) A methodological integrated approach to analyse climate change effects in agri-food sector: the TIMES water-energy-food module. Int J Environ Res Public Health 17:1–21. https://doi.org/10.3390/ijerph17217703

Tranfield D, Denyer D, Smart P (2003) Towards a methodology for developing evidenceinformed management knowledge by means of systematic review. Br J Manag 14:207–222

Trivellas P, Malindretos G, Reklitis P (2020) Implications of green logistics management on sustainable business and supply chain performance: evidence from a survey in the greek agri-food sector. Sustain 12:1–29. https://doi.org/10.3390/su122410515

Tsangas M, Gavriel I, Doula M et al (2020) Life cycle analysis in the framework of agricultural strategic development planning in the Balkan region. Sustain 12:1–15. https://doi.org/10.3390/su12051813

Ülgen VS, Björklund M, Simm N (2019) Inter-organizational supply chain interaction for sustainability : a systematic literature review.

UNEP S (2020) Guidelines for social life cycle assessment of products and organizations 2020.

UNEP/SETAC (2009) United Nations Environment Programme-society of Environmental Toxicology and Chemistry. Guidelines for social life cycle assessment of products. France

United Nations (2011) Guiding principles on business and human rights. Implementing the United Nations “protect, respect and remedy” framework

United Nations (2015) Transforming our world: the 2030 agenda for sustainable development. sustainabledevelopment.un.org

Van Asselt ED, Van Bussel LGJ, Van Der Voet H et al (2014) A protocol for evaluating the sustainability of agri-food production systems - a case study on potato production in peri-urban agriculture in the Netherlands. Ecol Indic 43:315–321. https://doi.org/10.1016/j.ecolind.2014.02.027

Van der Ploeg JD (2014) Peasant-driven agricultural growth and food sovereignty. J Peasant Stud 41:999–1030. https://doi.org/10.1080/03066150.2013.876997

van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84:523–538. https://doi.org/10.1007/s11192-009-0146-3

Van Eck NJ, Waltman L (2019) Manual for VOSviwer version 1.6.10. CWTS Meaningful metrics 1–53

Vasa L, Angeloska A, Trendov NM (2017) Comparative analysis of circular agriculture development in selected Western Balkan countries based on sustainable performance indicators. Econ Ann 168:44–47. https://doi.org/10.21003/ea.V168-09

Verdecho MJ, Alarcón-Valero F, Pérez-Perales D et al (2020) A methodology to select suppliers to increase sustainability within supply chains. Cent Eur J Oper Res. https://doi.org/10.1007/s10100-019-00668-3

Vergine P, Salerno C, Libutti A et al (2017) Closing the water cycle in the agro-industrial sector by reusing treated wastewater for irrigation. J Clean Prod 164:587–596. https://doi.org/10.1016/j.jclepro.2017.06.239

WCED (1987) Our common future - call for action

Webster K (2013) What might we say about a circular economy? Some temptations to avoid if possible. World Futures 69:542–554

Wheaton E, Kulshreshtha S (2013) Agriculture and climate change: implications for environmental sustainability indicators. WIT Trans Ecol Environ 175:99–110. https://doi.org/10.2495/ECO130091

Wijewickrama MKCS, Chileshe N, Rameezdeen R, Ochoa JJ (2021) Information sharing in reverse logistics supply chain of demolition waste: a systematic literature review. J Clean Prod 280:124359. https://doi.org/10.1016/j.jclepro.2020.124359

Woodhouse A, Davis J, Pénicaud C, Östergren K (2018) Sustainability checklist in support of the design of food processing. Sustain Prod Consum 16:110–120. https://doi.org/10.1016/j.spc.2018.06.008

Wu R, Yang D, Chen J (2014) Social Life Cycle Assessment Revisited Sustain 6:4200–4226. https://doi.org/10.3390/su6074200

Yadav S, Luthra S, Garg D (2021) Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks. Environ Sci Pollut Res 16633–16654. https://doi.org/10.1007/s11356-020-11676-1

Yee FM, Shaharudin MR, Ma G et al (2021) Green purchasing capabilities and practices towards Firm’s triple bottom line in Malaysia. J Clean Prod 307:127268. https://doi.org/10.1016/j.jclepro.2021.127268

Yigitcanlar T (2010) Rethinking sustainable development: urban management, engineering, and design. IGI Global

Zamagni A, Amerighi O, Buttol P (2011) Strengths or bias in social LCA? Int J Life Cycle Assess 16:596–598. https://doi.org/10.1007/s11367-011-0309-3

Download references

Author information

Authors and affiliations.

Department of Economy, Engineering, Society and Business Organization, University of “Tuscia, ” Via del Paradiso 47, 01100, Viterbo, VT, Italy

Cecilia Silvestri, Michela Piccarozzi & Alessandro Ruggieri

Department of Engineering, University of Rome “Niccolò Cusano, ” Via Don Carlo Gnocchi, 3, 00166, Rome, Italy

Luca Silvestri

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Cecilia Silvestri .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Communicated by Monia Niero

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: a number of ill-placed paragraph headings were removed and the source indication "Authors' elaborations" was added to Tables 1-3.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 31 KB)

Rights and permissions.

Reprints and permissions

About this article

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

Download citation

Received : 15 June 2021

Accepted : 16 February 2022

Published : 02 March 2022

DOI : https://doi.org/10.1007/s11367-022-02032-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Agri-food sector
  • Sustainability
  • Circular economy
  • Triple bottom line
  • Life cycle assessment
  • Find a journal
  • Publish with us
  • Track your research

IMAGES

  1. Qualitative vs Quantitative Research: Differences and Examples

    research methodology quantitative and qualitative

  2. Qualitative V/S Quantitative Research Method: Which One Is Better?

    research methodology quantitative and qualitative

  3. Qualitative vs. Quantitative Research: Definition and Types

    research methodology quantitative and qualitative

  4. Qualitative vs Quantitative Research: Differences and Examples

    research methodology quantitative and qualitative

  5. Qualitative vs Quantitative Research: What's the Difference?

    research methodology quantitative and qualitative

  6. A beginner’s guide to qualitative and quantitative research

    research methodology quantitative and qualitative

VIDEO

  1. Experimental Research

  2. NMIMS RESEARCH METHODOLOGY SAMPLE MCQs PART 13A

  3. Difference Between Quantitative and Qualitative Methodology and Which One I Chose

  4. QUANTITATIVE METHODOLOGY (Part 2 of 3):

  5. Characteristics of Quantitative and Qualitative Research

  6. Quantitative Research Methodology

COMMENTS

  1. Qualitative vs. Quantitative Research

    When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.

  2. Qualitative vs Quantitative Research: What's the Difference?

    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.

  3. A Practical Guide to Writing Quantitative and Qualitative Research

    Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...

  4. Difference Between Qualitative and Qualitative Research

    Qualitative Research Methods . 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.

  5. Qualitative vs. Quantitative Research: Comparing the Methods and

    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. ...

  6. Research Methods--Quantitative, Qualitative, and More: Overview

    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.

  7. What Is Quantitative Research?

    Revised on June 22, 2023. Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations. Quantitative research is the opposite of qualitative research, which involves collecting and analyzing ...

  8. Choosing the Right Research Methodology: A Guide

    Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. Qualitative research methodology: Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics.

  9. Quantitative and Qualitative Research Methods

    Quantitative research is " explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics). "*. Qualitative research seeks to answer questions about why and how people behave in the way that they do. It provides in-depth information about human behaviour.

  10. Quantitative and Qualitative Research Methods

    Qualitative research, in its broader sense, aims to describe, explore and understand phenomena through non-numerical based inquires and predominantly focuses on meanings, understandings and experiences. Qualitative research can be undertaken as a standalone study or when combined with quantitative research as a mixed methods study. Typically ...

  11. What Is Qualitative Research?

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

  12. Quantitative and Qualitative Research

    What is Quantitative Research? Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns.Quantitative research gathers a range of numeric data.

  13. The differences between qualitative and quantitative research methods

    Research is indispensable for understanding the world and advancing scientific knowledge.. 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.

  14. Quantitative vs. Qualitative Research: The Differences Explained

    There are two approaches to collecting and analyzing data: qualitative research and quantitative research. This video will explain the differences between th...

  15. How to use and assess qualitative research methods

    How to conduct qualitative research? Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [13, 14].As Fossey puts it: "sampling, data collection, analysis and interpretation are related to each other in a cyclical ...

  16. Quantitative and Qualitative Research

    When reviewing research resulting from your search, begin by reading the abstract. The abstract can help you identify the methodologies used in the study. Often research articles include a section that describes the methods used in more detail. Quantitative and qualitative methods are the two main categories of empirical research.

  17. Qualitative vs. Quantitative Research: What's the Difference?

    There are some significant benefits of qualitative research that should be considered when evaluating the difference between qualitative and quantitative research. The qualitative method allows for creativity, varied interpretations and flexibility. The scope of the research project can change as more information is gathered.

  18. Qualitative Vs. Quantitative Research

    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.

  19. Quantitative and Qualitative Research

    Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within their natural setting. It focuses on the "why" rather than the "what" of social phenomena and relies on the direct experiences of human beings as meaning-making agents in their every day lives.

  20. Qualitative Study

    Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants ...

  21. Quantitative research

    Qualitative methods might be used to understand the meaning of the conclusions produced by quantitative methods. Using quantitative methods, it is possible to give precise and testable expression to qualitative ideas. This combination of quantitative and qualitative data gathering is often referred to as mixed-methods research. Examples

  22. From Qualitative to Quantitative

    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 ...

  23. Quantitative and qualitative research methods: Considerations and

    Quantitative and qualitative research design represent the two sides of a coin in research project and Hammed (2020) citing Guba (1982) illustrated the axiomatic differences between the two ...

  24. Introduction to qualitative research methods

    INTRODUCTION. Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures.

  25. How to Balance Qualitative and Quantitative Data for Prospect Research

    1. Define your goals and criteria. 2. Use a mixed-methods approach. Be the first to add your personal experience. 3. Triangulate your data. Be the first to add your personal experience. 4.

  26. Toward a framework for selecting indicators of measuring ...

    Qualitative research makes up the most significant percentage of the sample (30 articles out of 39). The remaining nine articles are quantitative studies. ... The indicators identified by the authors are based on the application of a quantitative methodology that facilitates the analysis of the functioning of agri-food systems at the ...

  27. JMSE

    The present research quantitatively compared the fish composition among two methods for non-cryptic benthic fish species and one method for cryptobenthic fish species for the first time for the Mediterranean temperate reef fish assemblage. A visual census of fishes was performed within a cylinder of 4 m radius and within a cylinder of 2 m radius, while the cryptobenthic fishes were collected ...