experiment is qualitative or quantitative

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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

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.

• 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 numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Quantitative vs. Qualitative Research in Psychology

• 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.

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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.

Educational resources and simple solutions for your research journey

Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

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Qualitative Vs Quantitative Research – A Comprehensive Guide

Published by Carmen Troy at August 13th, 2021 , Revised On September 20, 2023

What is Quantitative Research?

Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.

Quantitative research isn’t simply based on  statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or questions, establish an appropriate research methodology, and draw findings & conclusions .

Characteristics of Quantitative Research

Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.

• Quantitative research tests or builds on other researchers’ existing theories whilst taking a reflective or extensive route.
• Quantitative research aims to test the research hypothesis or answer established research questions.
• It is primarily justified by positivist or post-positivist research paradigms.
• The  research design can be relationship-based, quasi-experimental, experimental, or descriptive.
• It draws on a small sample to make generalisations to a wider population using probability sampling techniques.
• Quantitative data is gathered according to the established research questions using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
• The researcher uses  statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or dissertation committee members might find it easier to verify your study results with numbers and statistical analysis.
• The study results’ accuracy is based on external and internal validity and authenticity of the data used.
• Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
• It underpins  research questions or hypotheses and findings to make conclusions.
• The researcher can provide recommendations for future research and expand or test existing theories.

What is Qualitative Research?

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a  question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

As the term suggests,  qualitative research  is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.

Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the  research process , how  research questions  are set up, how existing theories are built upon, what research methods are employed, and how the  findings  are unveiled to the readers.

You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies, case studies, and auto-ethnographies.

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Characteristics of Qualitative Research

Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.

• The research questions that you aim to answer will expand or even change as the  dissertation writing process continues . This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
• Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
• It can be based on various research models, such as critical theory, constructivism, and interpretivism.
• The chosen research design largely influences the analysis and discussion of results and the choices you make . Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
• Qualitative research answers research questions with theoretical sampling, where data gathered from the organisation or people are studied.
• It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews.
• It incorporates an  inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
• The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
• Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.
• Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to  draw general  conclusions .

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When to Use Qualitative and Quantitative Research Model?

• The research  title, research questions,  hypothesis , objectives, and study area generally determine the dissertation’s best research method.
• If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice because it might be easier for you to convince your supervisor or members of the dissertation committee with the use of statistics and numbers.
• On the other hand, oftentimes, statistics and a collection of numbers are not the answer, especially where there is a need to understand meanings, experiences, and beliefs.
• If your research questions or hypothesis can be better addressed through people’s observations and experiences, you should consider qualitative data.
• If you select an inappropriate research method, you will not prove your findings’ accuracy, and your dissertation will be pretty much meaningless. To prove that your research is authentic and reliable, choose a research method that best suits your study’s requirements.
• In the sections that follow, we explain the most commonly employed research methods for the dissertation, including quantitative, qualitative, and mixed research methods.

Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.

Here is an article that will help you  distinguish between primary and secondary research  and decide whether you need to use quantitative and/or qualitative methods of primary research in your dissertation.

Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory.

Limitations of Quantitative and Qualitative Research

What is quantitative research?

What is qualitative research.

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

Qualitative or quantitative, which research type should I use?

The research title, research questions, hypothesis , objectives, and study area generally determine the dissertation’s best research method.

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In historical research, a researcher collects and analyse the data, and explain the events that occurred in the past to test the truthfulness of observations.

Struggling to figure out “whether I should choose primary research or secondary research in my dissertation?” Here are some tips to help you decide.

Sampling methods are used to to draw valid conclusions about a large community, organization or group of people, but they are based on evidence and reasoning.

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Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather data in your research projects. So what do qualitative and quantitative mean exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research is all about language, expression, body language and other forms of human communication. That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people feel the way they do, why do they act in a certain way, what opinions do they have and what motivates them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data.

This qualitative data is called unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyze this structured data to assist computers and the human brain?

Free eBook: Quantitative and qualitative research design

What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

• Qualitative research:

Use this if your goal is to understand something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start  by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

• Quantitative research:

Use this if your goal is to test or confirm a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain insights around a topic and propose a hypothesis. Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted quantitative research could then reveal whether or not those stores selling children’s clothes achieve higher customer satisfaction scores and a rise in profits for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey question options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

Essay box (space for longer, more detailed written answers)

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

Likert Scale

Radio buttons (respondents choose just one option)

Check boxes (respondents can choose multiple options)

Sliding scale

Star rating

Analyzing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for quantitative data are now used for interpreting non numerical data too.

A rtificial intelligence programs can now be used to analyze open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organizations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyze than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

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Qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, request demo.

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Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

Need a helping hand?

Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena

Waoo! Simplifies language. I have read this several times and had probs. Today it is very clear. Bravo

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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

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

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

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

Table of contents

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

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

Prevent plagiarism, run a free check.

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analysing quantitative data

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

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

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

Analysing qualitative data

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

Some common approaches to analysing qualitative data include:

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

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

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

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

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

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

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

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

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

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

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

Edward barroga.

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

Glafera Janet Matanguihan

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

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

INTRODUCTION

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

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

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

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

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

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

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

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

Research questions in quantitative research

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

Hypotheses in quantitative research

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

Research questions in qualitative research

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

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

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

Hypotheses in qualitative research

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

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

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

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

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

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

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

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

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

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

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

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

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

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

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

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

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

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

Author Contributions:

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

The differences between qualitative and quantitative research methods

Last updated

15 January 2023

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

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• The differences between quantitative and qualitative research

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

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

• Qualitative vs. quantitative data collection

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

Quantitative data collection methods

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

Qualitative data collection methods

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

• Qualitative vs. quantitative outcomes

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

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

• How to analyze qualitative and quantitative data

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

Analyzing quantitative data

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

Analyzing qualitative data

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

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

• When to use qualitative vs. quantitative research

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

Qualitative research approach

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

Quantitative research approach

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

Mixed methods approach

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

• Benefits and limitations

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

Qualitative studies: Pros and cons

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

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

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

Quantitative studies: Pros and cons

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

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

• How to balance qualitative and quantitative research

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

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

Open-ended vs. close-ended questions

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

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

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

A. Purchase fewer items

B. Opt for less expensive choices

C. Take money from other parts of the budget

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

E. Make more food from scratch

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

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

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Qualitative and Quantitative Research — Explore the differences

In the research arena, there are two main approaches that researchers can take —  qualitative and quantitative research. Understanding the fundamentals of these two methods is crucial for conducting effective research and obtaining accurate results.

This article provides insights into the differences between qualitative and quantitative research and we also discuss how to develop research questions for qualitative and quantitative studies, and how to gather and analyze data using these research approaches. Furthermore, we will examine how to interpret findings from qualitative and quantitative research, as well as identify ethical considerations.

By the end of this comprehensive article, readers will be equipped with the knowledge and tools to apply qualitative and quantitative research to advance knowledge in their respective fields.

What is Qualitative and Quantitative Research?

Qualitative research aims to understand complex phenomena by exploring the subjective experiences and perspectives of individuals. It focuses on gathering in-depth data through techniques such as interviews, observations, and open-ended surveys. This approach allows researchers to delve into the intricacies of the topic, uncovering unique insights that may not be captured through quantitative methods alone.

For example, imagine a study on the impact of social media on mental health. Qualitative research would involve conducting interviews with individuals who have experienced negative effects from excessive social media use. Through these interviews, researchers can gain a deep understanding of the participants' experiences, emotions, and thoughts. They can explore the nuances of how social media affects different aspects of mental health, such as self-esteem, body image, and social comparison.

Conversely, quantitative research involves collecting numerical data and analyzing it using statistical methods to identify patterns, trends, and relationships. This approach allows researchers to generalize their findings to a larger population and calculate statistically significant results. It relies on structured surveys, experiments, and other data collection methods that provide standardized data for analysis.

Continuing with the example of social media and mental health, quantitative research would involve administering surveys to a large sample of individuals. The surveys would include questions that measure various aspects of mental health, such as anxiety, depression, and life satisfaction. By collecting numerical data from a large and diverse sample, researchers can identify trends and relationships between social media use and mental health outcomes.

Both qualitative and quantitative research have their strengths and weaknesses. Qualitative research allows for a deep understanding of the topic, providing rich insights and capturing the context of the participants' experiences. It allows researchers to uncover unique perspectives and shed light on subjective experiences.

On the other hand, quantitative research entails a structured and systematic approach to data collection and analysis, allowing for comparisons and generalizations across different groups and contexts.

However, it is crucial to emphasize that qualitative and quantitative research are not mutually exclusive. They frequently serve as a complement to one another within the realm of research studies. Researchers may use qualitative methods to explore a topic in-depth and generate hypotheses, which can then be tested using quantitative methods. This combination of approaches, known as mixed methods research, allows for a more comprehensive understanding of complex phenomena.

Advantages and Disadvantages of Each Research Method

Qualitative research offers the advantage of generating detailed and nuanced data. It allows researchers to explore complex issues and gain a deeper understanding of participants' thoughts, emotions, and behaviors. However, qualitative research can be time-consuming, and data analysis may be subjective.

In contrast, quantitative research provides objective and quantifiable data, making it easier to draw conclusions and establish causation. It enables researchers to collect data from large samples, increasing the generalizability of findings. Nevertheless, quantitative research may overlook important contextual information and fail to capture the complexities of human experiences. Additionally, it requires a solid understanding of statistical techniques for accurate analysis.

When to Use Qualitative or Quantitative Research?

The choice between qualitative and quantitative research depends on the research questions and objectives. Qualitative research is appropriate when exploring new or complex phenomena, seeking in-depth insights, or generating hypotheses for further investigation. It is particularly useful in social sciences and humanities. On the other hand, quantitative research is suitable when aiming to establish causal relationships, generalize findings to a larger population, or measure phenomena systematically and objectively. It is commonly employed in sciences such as psychology, economics, and medicine.

By considering the nature of the research question, the available resources, and the desired outcomes, researchers can make an informed decision on the appropriate research approach.

How to develop research Questions for Qualitative and Quantitative Studies?

A well-defined research question is essential for conducting meaningful research. In qualitative studies, research questions are exploratory and aim to understand the experiences, perceptions, and meanings of participants. These questions should be open-ended and allow for in-depth exploration of the phenomenon under investigation.

In quantitative research, research questions are often formulated to test hypotheses or examine relationships between variables. These questions should be clear, specific, and measurable to guide data collection and analysis.

Regardless of the research approach, it is crucial to develop research questions that align with the research objectives, is feasible to investigate and contribute to existing knowledge in the field.

Gathering and Analyzing Data

Qualitative research involves collecting data through various techniques, such as interviews, focus groups, and observations. Researchers must establish rapport with participants to encourage open and honest responses. The data collected is then analyzed using methods like thematic analysis and constant comparison to identify patterns, themes, and categories. In quantitative research, data is collected using surveys, experiments, or other structured methods. Researchers aim to obtain a representative sample and ensure the reliability and validity of the data. Statistical analysis techniques, such as descriptive statistics, correlation, and regression, are then applied to conclude.

Regardless of the research approach, it is essential to document the data collection and analysis process thoroughly to ensure transparency and reproducibility.

Interpreting Findings

Interpreting findings from qualitative research involves carefully analyzing the patterns, themes, and categories identified during data analysis. Researchers aim to understand the overarching meaning of the data and draw conclusions based on the participants' experiences and perspectives. The findings are often supported by direct quotes or examples from the data. In quantitative research, findings are interpreted by analyzing statistical results and examining the significance of relationships or differences. Researchers must carefully consider the limitations of the study and the generalizability of the findings. The results are often presented using tables, charts, and graphs for clarity.

Irrespective of the research approach, it is crucial to avoid generalizing beyond the scope of the data and to consider alternative interpretations.

Identifying Ethical Considerations in Qualitative and Quantitative Research

Both qualitative and quantitative research must adhere to ethical guidelines to protect the rights and well-being of participants. Researchers should obtain informed consent, ensure confidentiality, and prevent harm. In qualitative research, building trust and maintaining participant anonymity is crucial. In quantitative research, privacy and data protection are paramount.

Additionally, researchers must consider the potential biases, power dynamics, and conflicts of interest that may influence the research process and findings. Being aware of these ethical considerations helps ensure the integrity and reliability of the research.

How to Write a Research Report Based on Qualitative or Quantitative Data

When writing a research report, it is essential to structure it clearly and concisely. In qualitative research, the report typically includes an introduction, literature review, methodology, findings, discussion, and conclusion. The findings section focuses on the themes and patterns identified during analysis and is supported by quotes or examples from the data.

In quantitative research, the report generally consists of an introduction, literature review, methodology, results, discussion, and conclusion. The results section presents the statistical analysis and findings in a clear and organized manner, often using tables, charts, and graphs.

The report should be written in a scholarly tone, provide sufficient details, and communicate the research findings and implications.

Assessing Reliability and Validity of Qualitative and Quantitative Results

Reliability and validity are crucial considerations in research. In qualitative research, researchers can enhance reliability by using multiple researchers to analyze the data and compare their interpretations. Validity can be strengthened by employing rigorous data collection methods, establishing trustworthiness, and including participant validation.

In quantitative research, reliability can be assessed through test-retest reliability or inter-rater reliability. Validity can be evaluated by examining internal validity, external validity, and construct validity. Additionally, researchers should carefully consider potential confounding variables and ensure proper control measures are in place.

By assessing reliability and validity, researchers can enhance the credibility and trustworthiness of their research findings.

Qualitative and quantitative research are distinct yet complementary approaches to conducting research. Understanding when to use each method, developing appropriate research questions, gathering and analyzing data, interpreting findings, and addressing ethical considerations are all critical aspects of conducting valuable research. By embracing these methodologies and applying them appropriately, researchers can contribute to the advancement of knowledge and make meaningful contributions to their respective fields.

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Quantitative and Qualitative Research

• Quantitative vs. Qualitative Research
• Find quantitative or qualitative research in CINAHL
• Find quantitative or qualitative research in PsycINFO
• Relevant book titles

Mixed Methods Research

As its name suggests, mixed methods research involves using elements of both quantitative and qualitative research methods. Using mixed methods, a researcher can more fully explore a research question and provide greater insight. 

What is Empirical Research?

Empirical research is based on observed  and measured phenomena. Knowledge is extracted from real lived experience rather than from theory or belief. 

IMRaD: Scholarly journals sometimes use the "IMRaD" format to communicate empirical research findings.

Introduction:  explains why this research is important or necessary. Provides context ("literature review").

Methodology:  explains how the research was conducted ("research design").

Results: presents what was learned through the study ("findings").

Discussion:  explains or comments upon the findings including why the study is important and connecting to other research ("conclusion").

What is Quantitative Research?

Quantitative research gathers data that can be measured numerically and analyzed mathematically. Quantitative research attempts to answer research questions through the quantification of data. 

Indicators of quantitative research include:

contains statistical analysis 

large sample size 

objective - little room to argue with the numbers 

types of research: descriptive studies, exploratory studies, experimental studies, explanatory studies, predictive studies, clinical trials 

What is Qualitative Research?

Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups. 

Indicators of qualitative research include:

interviews or focus groups 

small sample size 

subjective - researchers are often interpreting meaning 

methods used: phenomenology, ethnography, grounded theory, historical method, case study 

Video: Empirical Studies: Qualitative vs. Quantitative

This video from usu libraries walks you through the differences between quantitative and qualitative research methods. (5:51 minutes) creative commons attribution license (reuse allowed)  https://youtu.be/rzcfma1l6ce.

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Explore Scholarships Colleges/Schools University Leadership Office of the President Academies at NU Course Catalog Accreditation Workforce and Community Education President’s Circle Board of Trustees NU Foundation Military & Veterans Coast Guard Space Force National Guard & Reservist Military Spouses & Dependents Military Resources NU proudly serves active duty and Veteran students from all branches of the military — at home, on base, and abroad. Military Admissions Online Degrees & Programs Consumer Information Student Login Graduation Events Student Portal Student Bookstore Student Resources Dissertation Boot Camp Show your NU pride and shop our online store for the latest and greatest NU apparel and accessories! Shop Now Request Info Our Programs What Is Qualitative vs. Quantitative Study? Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the “how” and “why” of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the “when” and “where,” utilizing tools like surveys, experiments, and statistical models to collect and analyze numerical data. In This Article: What is qualitative research, what is quantitative research. How Do Qualitative and Quantitative Research Differ? What’s the Difference Between a Qualitative and Quantitative Study? Analyzing qualitative and quantitative data, when to use qualitative or quantitative research, develop your research skills at national university. Qualitative and quantitative data are broad categories covering many research approaches and methods. While both share the primary aim of knowledge acquisition, quantitative research is numeric and objective, seeking to answer questions like when or where. On the other hand, qualitative research is concerned with subjective phenomena that can’t be numerically measured, like how different people experience grief. Having a firm grounding in qualitative and quantitative research methodologies will become especially important once you begin work on your dissertation or thesis toward the end of your academic program. At that point, you’ll need to decide which approach best aligns with your research question, a process that involves working closely with your Dissertation Chair. Keep reading to learn more about the difference between quantitative vs. qualitative research, including what research techniques they involve, how they approach the task of data analysis, and some strengths — and limitations — of each approach. We’ll also briefly examine mixed-method research, which incorporates elements of both methodologies. Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research, qualitative research is more exploratory, drawing upon data sources such as photographs, journal entries, video footage, and interviews. These features stand in stark contrast to quantitative research, as we’ll see throughout the remainder of this article. Quantitative research tackles questions from different angles compared to qualitative research. Instead of probing for subjective meaning by asking exploratory “how?” and “why?” questions, quantitative research provides precise causal explanations that can be measured and communicated mathematically. While qualitative researchers might visit subjects in their homes or otherwise in the field, quantitative research is usually conducted in a controlled environment. Instead of gaining insight or understanding into a subjective, context-dependent issue, as is the case with qualitative research, the goal is instead to obtain objective information, such as determining the best time to undergo a specific medical procedure. How Does Qualitative and Quantitative Research Differ? How are the approaches of quantitative and qualitative research different? In qualitative studies, data is usually gathered in the field from smaller sample sizes, which means researchers might personally visit participants in their own homes or other environments. Once the research is completed, the researcher must evaluate and make sense of the data in its context, looking for trends or patterns from which new theories, concepts, narratives, or hypotheses can be generated. Quantitative research is typically carried out via tools (such as questionnaires) instead of by people (such as a researcher asking interview questions). Another significant difference is that, in qualitative studies, researchers must interpret the data to build hypotheses. In a quantitative analysis, the researcher sets out to test a hypothesis. Both qualitative and quantitative studies are subject to rigorous quality standards. However, the research techniques utilized in each type of study differ, as do the questions and issues they hope to address or resolve. In quantitative studies, researchers tend to follow more rigid structures to test the links or relationships between different variables, ideally based on a random sample. On the other hand, in a qualitative study, not only are the samples typically smaller and narrower (such as using convenience samples), the study’s design is generally more flexible and less structured to accommodate the open-ended nature of the research. Below are a few examples of qualitative and quantitative research techniques to help illustrate these differences further. Sources of Quantitative Research Some example methods of quantitative research methods or sources include, but are not limited to, the following: Conducting polls, surveys, and experiments Compiling databases of records and information Observing the topic of the research, such as a specific reaction Performing a meta-analysis, which involves analyzing multiple prior studies in order to identify statistical trends or patterns Supplying online or paper questionnaires to participants The following section will cover some examples of qualitative research methods for comparison, followed by an overview of mixed research methods that blend components of both approaches. Sources of Qualitative Research Researchers can use numerous qualitative methods to explore a topic or gain insight into an issue. Some sources of, or approaches to, qualitative research include the following examples: Conducting ethnographic studies, which are studies that seek to explore different phenomena through a cultural or group-specific lens Conducting focus groups Examining various types of records, including but not limited to diary entries, personal letters, official documents, medical or hospital records, photographs, video or audio recordings, and even minutes from meetings Holding one-on-one interviews Obtaining personal accounts and recollections of events or experiences Examples of Research Questions Best Suited for Qualitative vs. Quantitative Methods Qualitative research questions:. How do patients experience the process of recovering from surgery? Why do some employees feel more motivated in remote work environments? What are the cultural influences on dietary habits among teenagers? Quantitative Research Questions: What is the average recovery time for patients after surgery? How does remote work impact employee productivity levels? What percentage of teenagers adhere to recommended dietary guidelines? These examples illustrate how qualitative research delves into the depth and context of human experiences, while quantitative research focuses on measurable data and statistical analysis. Mixed Methods Research In addition to the purely qualitative and quantitative research methods outlined above, such as conducting focus groups or performing meta-analyses, it’s also possible to take a hybrid approach that merges qualitative and quantitative research aspects. According to an article published by LinkedIn , “Mixed methods research avoids many [of the] criticisms” that have historically been directed at qualitative and quantitative research, such as the former’s vulnerability to bias, by “canceling the effects of one methodology by including the other methodology.” In other words, this mixed approach provides the best of both worlds. “Mixed methods research also triangulates results that offer higher validity and reliability.” If you’re enrolled as a National University student, you can watch a video introduction to mixed-method research by logging in with your student ID. Our resource library also covers qualitative and quantitative research methodologies and a video breakdown of when to use which approach. When it comes to quantitative and qualitative research, methods of collecting data differ, as do the methods of organizing and analyzing it. So what are some best practices for analyzing qualitative and quantitative data sets, and how do they call for different approaches by researchers? How to Analyze Qualitative Data Below is a step-by-step overview of how to analyze qualitative data. Make sure all of your data is finished being compiled before you begin any analysis. Organize and connect your data for consistency using computer-assisted qualitative data analysis software (CAQDAS). Code your data, which can be partially automated using a feedback analytics platform. Start digging deep into analysis, potentially using augmented intelligence to get more accurate results. Report on your findings, ideally using engaging aids to help tell the story. How to Analyze Quantitative Data There are numerous approaches to analyzing quantitative data. Some examples include cross-tabulation, conjoint analysis, gap analysis, trend analysis, and SWOT analysis, which refers to Strengths, Weaknesses, Opportunities, and Threats. Whichever system or systems you use, there are specific steps you should take to ensure that you’ve organized your data and analyzed it as accurately as possible. Here’s a brief four-step overview. Connect measurement scales to study variables, which helps ensure that your data will be organized in the appropriate order before you proceed. Link data with descriptive statistics, such as mean, median, mode, or frequency. Determine what measurement scale you’ll use for your analysis. Organize the data into tables and conduct an analysis using methods like cross-tabulation or Total Unduplicated Reach and Frequency (TURF) analysis. Simply knowing the difference between quantitative and qualitative research isn’t enough — you also need an understanding of when each approach should be used and under what circumstances. For that, you’ll need to consider all of the comparisons we’ve made throughout this article and weigh some potential pros and cons of each methodology. Pros and Cons of Qualitative Research Qualitative research has numerous strengths, but the research methodology is only more appropriate for some projects or dissertations. Here are some strengths and weaknesses of qualitative research to help guide your decision: Pro — More flex room for creativity and interpretation of results Pro — Greater freedom to utilize different research techniques as the study evolves Con — Potentially more vulnerable to bias due to their subjective nature Con — Sample sizes tend to be smaller and non-randomized Pros and Cons of Quantitative Research Quantitative research also comes with drawbacks and benefits, depending on what information you aim to uncover. Here are a few pros and cons to consider when designing your study. Pro — Large, random samples help ensure that the broader population is more realistically reflected Pro — Specific, precise results can be easily communicated using numbers Con — Data can suffer from a lack of context or personal detail around participant answers Con — Numerous participants are needed, driving up costs while posing logistical challenges If you dream of making a scientific breakthrough and contributing new knowledge that revolutionizes your field, you’ll need a strong foundation in research, from how it’s conducted and analyzed to a clear understanding of professional ethics and standards. By pursuing your degree at National University, you build stronger research skills and countless other in-demand job skills. With flexible course schedules, convenient online classes , scholarships and financial aid , and an inclusive military-friendly culture, higher education has never been more achievable or accessible. 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National University Chat Options 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. Qualitative research approaches Approach What does it involve? Grounded theory Researchers collect rich data on a topic of interest and develop theories . Researchers immerse themselves in groups or organizations to understand their cultures. Action research Researchers and participants collaboratively link theory to practice to drive social change. Phenomenological research Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences. Narrative research Researchers examine how stories are told to understand how participants perceive and make sense of their experiences. 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 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 data analysis Approach When to use Example To describe and categorize common words, phrases, and ideas in qualitative data. A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps. To identify and interpret patterns and themes in qualitative data. A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity. To examine the content, structure, and design of texts. A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade. To study communication and how language is used to achieve effects in specific contexts. A political scientist could use discourse analysis to study how politicians generate trust in election campaigns. 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. Here's why students love Scribbr's proofreading services Discover proofreading & editing 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 September 2, 2024, from https://www.scribbr.com/methodology/qualitative-research/ Is this article helpful? Pritha Bhandari Other students also liked, qualitative vs. quantitative research | differences, examples & methods, how to do thematic analysis | step-by-step guide & examples, what is your plagiarism score. Qualitative vs. Quantitative While quantitative research is based on numbers and mathematical calculations (aka quantitative data ), qualitative research is based on written or spoken narratives (or qualitative data ). Qualitative and quantitative research techniques are used in marketing , sociology , psychology , public health and various other disciplines. Comparison chart Qualitative versus Quantitative comparison chart Qualitative Quantitative Purpose The purpose is to explain and gain insight and understanding of phenomena through intensive collection of narrative data Generate hypothesis to be test , inductive. The purpose is to explain, predict, and/or control phenomena through focused collection of numerical data. Test hypotheses, deductive. Approach to Inquiry subjective, holistic, process- oriented Objective, focused, outcome- oriented Hypotheses Tentative, evolving, based on particular study Specific, testable, stated prior to particular study Research Setting Controlled setting not as important Controlled to the degree possible Sampling Purposive: Intent to select “small, ” not necessarily representative, sample in order to get in-depth understanding Random: Intent to select “large, ” representative sample in order to generalize results to a population Measurement Non-standardized, narrative (written word), ongoing Standardized, numerical (measurements, numbers), at the end Design and Method Flexible, specified only in general terms in advance of study Nonintervention, minimal disturbance All Descriptive— History, Biography, Ethnography, Phenomenology, Grounded Theory, Case Study, (hybrids of these) Consider many variable, small group Structured, inflexible, specified in detail in advance of study Intervention, manipulation, and control Descriptive Correlation Causal-Comparative Experimental Consider few variables, large group Data Collection Strategies Document and artifact (something observed) that is collection (participant, non-participant). Interviews/Focus Groups (un-/structured, in-/formal). Administration of questionnaires (open ended). Taking of extensive, detailed field notes. Observations (non-participant). Interviews and Focus Groups (semi-structured, formal). Administration of tests and questionnaires (close ended). Data Analysis Raw data are in words. Essentially ongoing, involves using the observations/comments to come to a conclusion. Raw data are numbers Performed at end of study, involves statistics (using numbers to come to conclusions). Data Interpretation Conclusions are tentative (conclusions can change), reviewed on an ongoing basis, conclusions are generalizations. The validity of the inferences/generalizations are the reader’s responsibility. Conclusions and generalizations formulated at end of study, stated with predetermined degree of certainty. Inferences/generalizations are the researcher’s responsibility. Never 100% certain of our findings. Type of data Qualitative research gathers data that is free-form and non-numerical, such as diaries, open-ended questionnaires, interviews and observations that are not coded using a numerical system. On the other hand, quantitative research gathers data that can be coded in a numerical form. Examples of quantitative research include experiments or interviews/questionnaires that used closed questions or rating scales to collect information . Applications of Quantitative and Qualitative Data Qualitative data and research is used to study individual cases and to find out how people think or feel in detail. It is a major feature of case studies. Quantitative data and research is used to study trends across large groups in a precise way. Examples include clinical trials or censuses. When to use qualitative vs. quantitative research? Quantitative and qualitative research techniques are each suitable in specific scenarios. For example, quantitative research has the advantage of scale. It allows for vast amounts of data to be collected -- and analyzed -- from a large number of people or sources. Qualitative research, on the other hand, usually does not scale as well. It is hard, for example, to conduct in-depth interviews with thousands of people or to analyze their responses to open-ended questions. But it is relatively easier to analyze survey responses from thousands of people if the questions are closed-ended and responses can be mathematically encoded in, say, rating scales or preference ranks. Conversely, qualitative research shines when it is not possible to come up with closed-ended questions. For example, marketers often use focus groups of potential customers to try and gauge what influences brand perception, product purchase decisions, feelings and emotions . In such cases, researchers are usually at very early stages of forming their hypotheses and do not want to limit themselves to their initial understanding. Qualitative research often opens up new options and ideas that quantitative research cannot due to its closed-ended nature. Analysis of data Qualitative data can be difficult to analyze, especially at scale, as it cannot be reduced to numbers or used in calculations. Responses may be sorted into themes, and require an expert to analyze. Different researchers may draw different conclusions from the same qualitative material. Quantitative data can be ranked or put into graphs and tables to make it easier to analyze. Data Explosion Data is being generated at an increasing rate because of the expansion in the number of computing devices and the growth of the Internet . Most of this data is quantitative and special tools and techniques are evolving to analyze this " big data ". Effects of Feedback The following diagram illustrates the effects of positive and negative feedback on Qualitative vs Quantitative research: Qualitative Quantitative - Simply Psychology Qualitative and Quantitative Research - University of Oxford Related Comparisons Share this comparison via: If you read this far, you should follow us: "Qualitative vs Quantitative." Diffen.com. Diffen LLC, n.d. Web. 16 May 2024. < > Comments: Qualitative vs Quantitative Sales vs Marketing Advertising vs Promotion Mergers vs Acquisitions Vision vs Mission Statement Edit or create new comparisons in your area of expertise. Stay connected © All rights reserved. Contact Us! Qualitative and Quantitative Research In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach.  The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories.    Complexity, contextual, inductive logic, discovery, exploration Experiment, random assignment, independent/dependent variable, causal/correlational, validity, deductive logic Understand a phenomenon Discover causal relationships or describe a phenomenon Purposive sample, small Random sample, large Focus groups, interviews, field observation Tests, surveys, questionnaires Phenomenological, grounded theory, ethnographic, case study, historical/narrative research, participatory research, clinical research Experimental, quasi-experimental, descriptive, methodological, exploratory, comparative, correlational, developmental (cross-sectional, longitudinal/prospective/cohort, retrospective/ex post facto/case control) Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies. References: LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE Quick Overview This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research. It's Not Always One or the Other! It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods.  Next: Nursing >> Last Updated: May 28, 2024 4:52 PM URL: https://stevenson.libguides.com/c.php?g=236343 Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights. Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes. Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups. Qualitative vs. Quantitative Research in Education: Definitions Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics. Generate Hypotheses with Qualitative Research Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles. Form Strong Conclusions with Quantitative Research Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis. Differences in Data Collection Methods Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal. Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address. An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form. Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language. On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner. Qualitative Research Methods Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews. Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants. Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment. Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject. Quantitative Research Methods Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic. Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships. Observations : Researchers look at quantifiable patterns and behavior. Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data. Choosing a Research Strategy When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in. For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing. It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ. In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data. Learn How to Put Education Research into Action Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership . What’s the Difference Between Educational Equity and Equality? EdD vs. PhD in Education: Requirements, Career Outlook, and Salary Top Education Technology Jobs for Doctorate in Education Graduates American University, EdD in Education Policy and Leadership Edutopia, “2019 Education Research Highlights” Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities” iMotion, “Qualitative vs. Quantitative Research: What Is What?” Scribbr, “Qualitative vs. Quantitative Research” Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?” Typeform, “A Simple Guide to Qualitative and Quantitative Research” Request Information AU Program Helper This AI chatbot provides automated responses, which may not always be accurate. By continuing with this conversation, you agree that the contents of this chat session may be transcribed and retained. You also consent that this chat session and your interactions, including cookie usage, are subject to our  privacy policy . Advertisement Qualitative vs. Quantitative: Key Differences in Research Types Share Content on Facebook Share Content on LinkedIn Share Content on Flipboard Share Content on Reddit Share Content via Email Let's say you want to learn how a group will vote in an election. You face a classic decision of gathering qualitative vs. quantitative data. With one method, you can ask voters open-ended questions that encourage them to share how they feel, what issues matter to them and the reasons they will vote in a specific way. With the other, you can ask closed-ended questions, giving respondents a list of options. You will then turn that information into statistics. Neither method is more right than the other, but they serve different purposes. Learn more about the key differences between qualitative and quantitative research and how you can use them. What Is Qualitative Research? What is quantitative research, qualitative vs. quantitative research: 3 key differences, benefits of combining qualitative and quantitative research. Qualitative research aims to explore and understand the depth, context and nuances of human experiences, behaviors and phenomena. This methodological approach emphasizes gathering rich, nonnumerical information through methods such as interviews, focus groups , observations and content analysis. In qualitative research, the emphasis is on uncovering patterns and meanings within a specific social or cultural context. Researchers delve into the subjective aspects of human behavior , opinions and emotions. This approach is particularly valuable for exploring complex and multifaceted issues, providing a deeper understanding of the intricacies involved. Common qualitative research methods include open-ended interviews, where participants can express their thoughts freely, and thematic analysis, which involves identifying recurring themes in the data. Examples of How to Use Qualitative Research The flexibility of qualitative research allows researchers to adapt their methods based on emerging insights, fostering a more organic and holistic exploration of the research topic. This is a widely used method in social sciences, psychology and market research. Here are just a few ways you can use qualitative research. To understand the people who make up a community : If you want to learn more about a community, you can talk to them or observe them to learn more about their customs, norms and values. To examine people's experiences within the healthcare system : While you can certainly look at statistics to gauge if someone feels positively or negatively about their healthcare experiences, you may not gain a deep understanding of why they feel that way. For example, if a nurse went above and beyond for a patient, they might say they are content with the care they received. But if medical professional after medical professional dismissed a person over several years, they will have more negative comments. To explore the effectiveness of your marketing campaign : Marketing is a field that typically collects statistical data, but it can also benefit from qualitative research. For example, if you have a successful campaign, you can interview people to learn what resonated with them and why. If you learn they liked the humor because it shows you don't take yourself too seriously, you can try to replicate that feeling in future campaigns. Types of Qualitative Data Collection Qualitative data captures the qualities, characteristics or attributes of a subject. It can take various forms, including: Audio data : Recordings of interviews, discussions or any other auditory information. This can be useful when dealing with events from the past. Setting up a recording device also allows a researcher to stay in the moment without having to jot down notes. Observational data : With this type of qualitative data analysis, you can record behavior, events or interactions. Textual data : Use verbal or written information gathered through interviews, open-ended surveys or focus groups to learn more about a topic. Visual data : You can learn new information through images, photographs, videos or other visual materials. Quantitative research is a systematic empirical investigation that involves the collection and analysis of numerical data. This approach seeks to understand, explain or predict phenomena by gathering quantifiable information and applying statistical methods for analysis. Unlike qualitative research, which focuses on nonnumerical, descriptive data, quantitative research data involves measurements, counts and statistical techniques to draw objective conclusions. Examples of How to Use Quantitative Research Quantitative research focuses on statistical analysis. Here are a few ways you can employ quantitative research methods. Studying the employment rates of a city : Through this research you can gauge whether any patterns exist over a given time period. Seeing how air pollution has affected a neighborhood : If the creation of a highway led to more air pollution in a neighborhood, you can collect data to learn about the health impacts on the area's residents. For example, you can see what percentage of people developed respiratory issues after moving to the neighborhood. Types of Quantitative Data Quantitative data refers to numerical information you can measure and count. Here are a few statistics you can use. Heights, yards, volume and more : You can use different measurements to gain insight on different types of research, such as learning the average distance workers are willing to travel for work or figuring out the average height of a ballerina. Temperature : Measure in either degrees Celsius or Fahrenheit. Or, if you're looking for the coldest place in the universe , you may measure in Kelvins. Sales figures : With this information, you can look at a store's performance over time, compare one company to another or learn what the average amount of sales is in a specific industry. Quantitative and qualitative research methods are both valid and useful ways to collect data. Here are a few ways that they differ. Data collection method : Quantitative research uses standardized instruments, such as surveys, experiments or structured observations, to gather numerical data. Qualitative research uses open-ended methods like interviews, focus groups or content analysis. Nature of data : Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data. Sampling : Quantitative research involves larger sample sizes to ensure statistical validity and generalizability of findings to a population. With qualitative research, it's better to work with a smaller sample size to gain in-depth insights into specific contexts or experiences. You can simultaneously study qualitative and quantitative data. This method , known as mixed methods research, offers several benefits, including: A comprehensive understanding : Integration of qualitative and quantitative data provides a more comprehensive understanding of the research problem. Qualitative data helps explain the context and nuances, while quantitative data offers statistical generalizability. Contextualization : Qualitative data helps contextualize quantitative findings by providing explanations into the why and how behind statistical patterns. This deeper understanding contributes to more informed interpretations of quantitative results. Triangulation : Triangulation involves using multiple methods to validate or corroborate findings. Combining qualitative and quantitative data allows researchers to cross-verify results, enhancing the overall validity and reliability of the study. This article was created in conjunction with AI technology, then fact-checked and edited by a HowStuffWorks editor. Please copy/paste the following text to properly cite this HowStuffWorks.com article: Cookie consent We use our own and third-party cookies to show you more relevant content based on your browsing and navigation history. Please accept or manage your cookie settings below. Here's our   cookie policy Product overview What is Typeform? Form builder Signups and orders Survey maker Research and feedback Quiz maker Trivia and product match Typeform for Growth For B2B marketers Product updates Latest feature releases HubSpot for Typeform Our new partner Sales & marketing misalignment Check out our latest ebook Human resources Customer success Acquire customers Get feedback Do research Help center Find quick answers Community Share and interact Contact us Speak to our team Partners Browse or join Careers Join our team → Qualitative vs. quantitative research... Qualitative vs. quantitative research: A simple guide Quantitative research deals with numbers and statistics, while qualitative research involves pulling information from experiences and stories. Latest posts on Tips Typeform    |    08.2024 Typeform    |    07.2024 From Tesla to Tushy, every successful brand is built on a foundation of both quantitative and qualitative research. Marketers and product developers use this zero-party data to frame their advertising strategies, product positioning, and brand voice—basically, everything that goes into designing and selling a product or service. When it comes to qualitative vs. quantitative research, both methods have their benefits and drawbacks in certain applications. We break down what you need to know before running your next round of market research.  Qualitative vs. quantitative research: What’s the difference? Quantitative research counts and measures numbers to find statistical patterns, while qualitative research is a deep dive into understanding people’s thoughts and experiences. They're similar in that they both aim to uncover valuable insights, but they use different tools and approaches to do so. But don’t be fooled into thinking that one research method is better than the other—both require systematically applied research methods and analysis.   Qualitative Research Quantitative Research Goal Understand reasons or trends Quantify or measure data Sample size Smaller, often nonrepresentative Larger Analysis Nonstatistical Statistical Question type Open-ended Close-ended Response type Personalized Predetermined What is qualitative research and data? Qualitative research is like the Sherlock Holmes of the research world—it seeks to uncover the hidden stories, motivations, and intricacies that numbers can't reveal. Instead of crunching data, it dives deep into people's experiences, thoughts, and feelings to help explain certain behaviors and patterns.  In qualitative research, it's not about numbers but rather words, pictures, and observations. You'll collect rich, unstructured data via interviews, focus group discussions, or open-ended surveys.  Say you're a marketing rep keen on understanding how people perceive your smartphone brand.  First, you organize a series of in-depth interviews with smartphone users, asking open-ended questions about their experiences with the brand. Participants share stories about their interactions, likes, dislikes, and emotional connections with the product. You also delve into social media posts, online reviews, and forum discussions to gauge the brand's online reputation. As you analyze this data, patterns begin to emerge. You find that users consistently describe the brand as "innovative" and "user-friendly." However, you also discover a recurring frustration with battery life and customer support. Qualitative research not only provides you with insights into how people perceive the brand but also dives into the emotional nuances behind their perceptions. Armed with this knowledge, you can fine-tune your advertising campaigns and product improvements to align with your target audience's genuine feelings and experiences. Pros and cons of qualitative research Qualitative research is your go-to when you want to explore the human side of data. It's like having a heart-to-heart conversation with your research subjects. Just keep in mind that, like any detective work, it comes with its own quirks and challenges. Deep insights: It's great at uncovering the "whys" and "hows" behind human behavior, providing rich insights that quantitative data can miss. Flexible and exploratory: Qualitative research allows for flexibility, so you can adapt your questions and approach when you face the unexpected. Humanizing data: Unlike numbers, qualitative research humanizes data by bringing stories and personal experiences to the forefront. It's perfect for capturing human nuances and emotions. Subjectivity: Different researchers might draw different conclusions from the same data based on their own personal feelings, experiences, or opinions, so it's crucial to stay aware of potential bias. Resource-intensive: Qualitative research demands time and effort. Conducting interviews, transcribing, and analyzing data is a labor-intensive process, which might not suit all budgets or timelines. Smaller samples: Your pool of participants tends to be smaller compared to quantitative research, making it challenging to generalize findings to a larger population. It's like diving deep into a few personal stories rather than looking at the bigger picture. Can’t always be automated: Unlike quantitative research, where you can automate data collection and analysis with software, qualitative research relies heavily on human interaction and interpretation. You can, however, create a survey with open-ended questions to collect qualitative data. Better yet, try our VideoAsk feature, which allows you to ask questions via pre-recorded video and lets respondents answer in video, voice, or text format, preserving that ever-important human element that defines qualitative data.  What is quantitative research and data? Quantitative research is all about numbers, statistics, and cold, hard data. It’s more structured and objective and helps reduce researcher biases . It gets at the “what” of a person’s behavior by answering questions like how many, how often, and to what extent? Let’s look at quantitative research in action. Imagine you're trying to pinpoint the target market for your new fitness app. You survey the app's users, collecting data on their age, gender, location, and fitness habits. The data reveals that 75% of your target users are ages 18-34, with a nearly even split between men and women. You also notice that users in urban areas are 20% more likely to use your app regularly than those in rural areas. Quantitative research doesn't stop at just counting, though. It's also about analyzing data to spot trends and differences. In this case, it's clear that your core audience consists of younger adults in urban settings, and you can tailor your marketing strategies and app features to better cater to this demographic. So, if you're a number-crunching, stats-loving kind of researcher, quantitative research is your jam. Pros and cons of quantitative research In a nutshell, quantitative research is your go-to when you want solid, numerical answers. But remember, it won't tell you the whole story, and sometimes, life's questions are a bit too complex for a numbers-only approach. Keep these pros and cons in mind when running your next quantitative study: Precision with numbers: Quantitative research is like a laser-guided missile for numbers. It offers precise measurements and statistical analysis, which is great when you need concrete answers. Reproducibility: It's a cookie-cutter approach—your methods and results can be replicated by others, making it a cornerstone of scientific rigor. Generalizability: You can often apply findings to a larger population—if it works for one group, it might work for a similar one. Limited bias: Quantitative research can be a bias-buster. With structured surveys, standardized data collection methods, and statistical analysis, it's easier to minimize researcher bias and keep the study objective.  Fewer resources: If you're watching your budget, quantitative research may give you more bang for your buck. It often requires fewer resources in terms of time, personnel, and money, making it a practical choice, especially for smaller-scale research projects. Limited depth: While it's king of numbers, quantitative research can be a bit shallow in understanding. It's like knowing the “what” but not the “why.” Context ignored: Sometimes context gets lost in a sea of numbers, and you might miss the bigger picture. Inflexibility: If your research question isn't easily quantifiable, you might end up with results that are difficult to decipher. Not everything can be counted or measured. Which is better: Qualitative or quantitative research? It’s a trick question. We’re not pitting qualitative and quantitative research against each other. However, one may prove more useful than the other, depending on your research goals.  For example, it’s best to stick with qualitative research when: You want to explore in-depth: Choose qualitative research when you need a deep understanding of a complex phenomenon, like customer perceptions or human behavior. It's like peeling back the layers of an onion to uncover the core. You need to generate hypotheses: Qualitative research is fantastic for generating ideas or hypotheses that you can later test with quantitative research.  You value the human perspective: If you want to capture emotions, stories, and personal experiences, opt for qualitative research. It's your go-to when you're interested in "the why" rather than just "the what." On the other hand, quantitative research may prove more valuable if: You need to measure and quantify: If you're after hard numbers, like percentages, averages, or correlations, quantitative research is your go-to. You want to generalize to a larger population: Quantitative research allows you to make statistically valid generalizations to a broader audience. If you plan to reach a wide market, this is your best bet. You prefer structured and standardized data collection: When consistency and minimizing bias are critical, quantitative research methods like surveys and online tests provide a structured and uniform approach.  However, you aren’t limited to just one type of research method. You can use both qualitative and quantitative data to give you the most insightful information when: You need a comprehensive understanding: Sometimes, using both qualitative and quantitative research sequentially is the ideal approach. Start with qualitative research to explore a topic, identify key variables, and generate hypotheses. Then, use quantitative research to test those hypotheses on a larger scale, ensuring a more comprehensive understanding. You want to validate findings: When you've conducted qualitative research and want to make sure your findings are not just anecdotal, quantitative research can validate and generalize your insights to a broader population. You're tackling a complex problem: For multifaceted issues, using both approaches can provide a well-rounded view. Qualitative research can uncover the depth and nuances, while quantitative research can quantify the extent of the issue and help prioritize actions. Quantitative research provides evidence and predictions. Qualitative research provides context and explanations. So which one is best for you? That depends on the questions you need answered. Research methods Quantitative and qualitative research methods are systematic ways of collecting data and testing hypotheses. And guess what? It’s something you already do all the time. We constantly take in information from our surroundings to figure out how to interact with the people around us. The same goes for market research . A company tries to learn more about their customers and the market. Why? To develop an effective marketing plan or tweak one they already have. The method you use to do this depends on the data that will answer your key questions. Qualitative research methods Here are some of the most common qualitative research methods: In-depth interviews: Known as IDI in market research circles, in-depth interviews are ideal for digging into people’s attitudes and experiences.  Case studies: In-depth analysis of a single case or a few cases are best suited for investigating unique or complex cases in depth Focus groups: These are effective for getting several opinions in a conversational format. Participants lead the discussion, while a facilitator guides the conversation through a list of topics, questions, or projective exercises. Participant observation: Simply engaging and observing your audience day-to-day provides a firsthand view of how people interact in real-life situations. Historical research: Exploring historical documents and records helps you examine the past through primary and secondary sources, contributing to our understanding of historical events and trends and how they may relate to the current scenario. Qualitative surveys: Surveys comprised of open-ended questions provide an automated way to receive qualitative data through a quantitative approach.. Ethnography: Ethnography is a broad market research approach that involves all of the methods above in order to gain a comprehensive understanding of the culture or community being studied.  Quantitative research methods Here are some of the most common quantitative research methods: Surveys: Surveys conducted online, over the phone, and even in person with structured interview questionnaires are an efficient way of collecting data from a large pool of participants.  Polls: Polls are one- or two-question surveys that are often used to gauge public opinion on an important matter (or a frivolous matter—it’s your poll). Because polls are only one or two questions, analysis is pretty much immediate. Structured observation: This is a structured form of ethnography used to measure certain actions or behaviors, such as tracking how many boxes of cereal people pick up before choosing one to purchase. Experiment: Market researchers conduct controlled, manipulated, or randomized experiments to understand how specific variables influence outcomes through methods like A/B testing or pilot testing. Quizzes: Answering a few general questions to find out which Harry Potter character you are may seem like fun and games, but interactive quizzes are a great tool for gathering information while keeping your audience engaged.  Secondary data analysis: This cost-effective research method taps into big existing datasets like government databases or company records to pull relevant data.  Mixed research methods Mixed research methods combine both qualitative and quantitative approaches to provide a comprehensive understanding of the question at hand. Some of the most common mixed research methods include: User testing: You’ve heard the phrase “Show, don’t tell.” So rather than asking people to explain their experiences, why not have them show you? User testing can tell you where you thrive and fall short, so you can adjust your marketing strategy accordingly. Help transcripts: Live chat or call transcripts can yield both qualitative and quantitative data. Reading and coding them can help you understand people’s pain points and challenges throughout your conversion funnel. Customer reviews: Look beyond your own surveys and check sites like Yelp or Google reviews. What are people saying about you? What do they like and dislike? The things people say and how often they say it can yield robust qualitative and quantitative data. Data analysis Data analysis is the search for patterns in data, followed by the interpretation of that information to help explain why those patterns are there. It’s important to keep in mind that quantitative and qualitative data aren't mutually exclusive. Qualitative data can be translated into quantitative data. For example, you could count the number of times interviewees used a particular word to describe your product to yield quantitative data. Similarly, quantitative methods of analysis require you to explain what the patterns mean and connect them to other parts of your business—a qualitative exercise! Qualitative data analysis example Qualitative data can be difficult to analyze since it’s largely made up of text, images, videos, and open-ended responses instead of numbers. Examples of qualitative data analysis include: Thematic analysis: Identifying and categorizing recurring themes, patterns, or concepts within the data to uncover the most prevalent and significant themes in your dataset Content analysis: Examining large amounts of text, visuals, or audio content to identify themes or patterns  Discourse analysis: Dissecting the language used in the data to understand how individuals or groups construct meaning and social reality through their discourse Cross-case analysis: Comparing and contrasting multiple cases to identify commonalities and differences, helping to develop broader insights Quantitative data analysis example Quantitative data analysis is all about crunching numbers. It can involve presenting data models such as graphs, charts, tables, probabilities, and more. Tools like Excel, R, and Stata make it easy to track quantitative data like: Average scores and means The number of times a specific response is recorded Connections or potential cause-and-effect relationships between two or more variables The reliability and validity of results  Get the right data with Typeform Congrats—you’ve learned all about the differences between qualitative vs. quantitative research. Now, the key to successful data collection is iteration. That doesn’t mean doing the same thing again and again. It means continually returning to your questions, methods, and data to spark new ideas and insights that'll level up your research —and your business. Typeform makes it easy to design and automate forms that collect both quantitative and qualitative data—no extensive interviews or focus groups required. With conditional formatting and various question types, you can gather the information you need to get more customers. About the author Lydia is a content marketer with experience across both the B2B and B2C landscapes. Besides marketing and content, she's really into her dog Louie. Liked that? Check these out: Social media quizzes: 10 tips from Brainbeats “Is the music you listen to actually right for you?” Find out how an interactive quiz can help you conquer Facebook. Simon Dumont    |    12.2018 What is descriptive research? Definition, examples, and use cases Descriptive research is a research methodology that focuses on understanding the particular characteristics of a group, phenomenon, or experience. Connected, not competing: How lead generation and customer acquisition work together Lead generation and customer acquisition are different pieces of the same puzzle. But how exactly do they work together? Learn more about how to harness the power of both to create a seamless customer (and data) journey. Typeform    |    06.2024 Canvas | University | Ask a Librarian Library Homepage Arrendale Library Empirical & Non-Empirical Research Quantitative vs. Qualitative Empirical Research What's the Difference Between Qualitative and Quantitative? Distinguishing quantitative & qualitative methods, word clues to identify methods. Reference Works for Social Sciences Research What is Non-Empirical Research? Contact Us!  Call us at 706-776-0111   Chat with a Librarian   Send Us Email   Library Hours What’s the Difference Between Qualitative and Quantitative Methods? Tests hypotheses born from theory Generates understanding from patterns Generalizes from a sample to the population Applies ideas across contexts Focuses on control to establish cause or permit prediction Focuses on interpreting and understanding a social construction of meaning in a natural setting Attends to precise measurements and objective data collection Attends to accurate description of process via words, texts, etc., and observations Favors parsimony and seeks a single truth Appreciates complexity and multiple realities Conducts analysis that yields a significance level Conducts analysis that seeks insight and metaphor Faces statistical complexity Faces conceptual complexity Conducts analysis after data collection Conducts analysis along with data collection Favors the laboratory Favors fieldwork Uses instruments with psychometric properties Relies on researchers who have become skilled at observing, recording, and coding (researcher as instrument) Generates a report that follows a standardized format Generates a report of findings that includes expressive language and a personal voice Uses designs that are fixed prior to data collection Allows designs to emerge during study Often measures a single-criterion outcome (albeit multidimensional) Offers multiple sources of evidence (triangulation) Often uses large sample sizes determined by power analysis or acceptable margins of error Often studies single cases or small groups that build arguments for the study's confirmability Uses statistical scales as data Uses text as data Favors standardized tests and instruments that measure constructs Favors interviews, observations, and documents Performs data analysis in a prescribed, standardized, linear fashion Performs data analysis in a creative, iterative, nonlinear, holistic fashion Uses reliable and valid data Uses trustworthy, credible, coherent data From: Suter, W. N. (2012). Qualitative Data, Analysis, and Design. In  Introduction to educational research: A critical thinking approach . SAGE Publications, Inc., www.galileo.usg.edu/redirect?inst=pie1&url=https://dx.doi.org/10.4135/9781483384443 The words in this table can be used to evaluate whether an article tends more toward the quantitative or qualitative domain. Well-written article abstracts will contain words like these to succinctly characterize the article's content. Adapted from: McMillan, J. H. (2012).  Educational research: Fundamentals for the consumer  (6th ed.). Boston, MA: Pearson. 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Open access Published: 02 September 2024 Implementation of health-promoting retail initiatives in the Healthier Choices in Supermarkets Study—qualitative perspectives from a feasibility study Katrine Sidenius Duus   ORCID: orcid.org/0000-0002-1630-3132 1 , Tine Tjørnhøj-Thomsen   ORCID: orcid.org/0000-0003-3621-6682 1 & Rikke Fredenslund Krølner   ORCID: orcid.org/0000-0002-4928-4310 1   BMC Medicine volume  22 , Article number:  349 ( 2024 ) Cite this article Metrics details Improving food environments like supermarkets has the potential to affect customers’ health positively. Scholars suggest researchers and retailers collaborate closely on implementing and testing such health-promoting interventions, but knowledge of the implementation of such interventions is limited. We explore the implementation of four health-promoting food retail initiatives selected and developed by a partnership between a research institution, a large retail group, and a non-governmental organisation. The four initiatives included downsizing of bags for pick’n’ mix sweets and soda bottles at the check-out registers, shelf tags promoting healthier breakfast cereal options, and replacing a complimentary bun with a banana offered to children. The initiatives were implemented for 6 weeks (or longer if the store manager allowed it) in one store in Copenhagen, Denmark. Data were collected through observations, informal interviews with customers, and semi-structured interviews with retailers. We conducted a thematic analysis of transcripts and field notes inspired by process evaluation concepts and included quantitative summaries of selected data. Two out of four initiatives were not implemented as intended. The implementation was delayed due to delivery issues, which also resulted in soda bottles not being downsized as intended. The maintenance of the shelf tags decreased over time. Retailers expressed different levels of acceptability towards the initiatives, with a preference for the complimentary banana for children. This was also the only initiative noticed by customers with both positive and negative responses. Barriers and facilitators of implementation fell into three themes: Health is not the number one priority, general capacity of retailers, and influence of customers and other stakeholders on store operation. Conclusions The retailers’ interests, priorities, and general capacity influenced the initiative implementation. Retailers’ acceptability of the initiatives was mixed despite their involvement in the pre-intervention phase. Our study also suggests that customer responses towards health-promoting initiatives, as well as cooperation with suppliers and manufacturers in the development phase, may be determining to successful implementation. Future studies should explore strategies to facilitate implementation, which can be applied prior to and during the intervention. Peer Review reports What we eat affects our health and well-being [ 1 ]. Diet is associated with obesity, cancers [ 2 ], and mental well-being [ 3 ], and a healthy diet has been associated with lower all-cause mortality [ 4 ]. One important factor in improving diet is to create a food environment that supports a healthy diet [ 5 , 6 ]. In modern societies, such as Denmark, supermarkets are the main source of food [ 7 ]. Supermarkets therefore hold a significant influence on what food we buy and potentially also eat [ 7 , 8 , 9 ]. Studies report associations between the concentration of supermarkets and overweight and obesity in the neighbourhood [ 10 ] and between the healthfulness of supermarkets and people’s diets [ 11 , 12 ]. Moreover, unhealthy food and beverage products are promoted more often than healthy products and beverages in, for example, supermarkets [ 9 , 13 , 14 ]. This indicates a need to explore how and if it is possible to implement health promotion initiatives in supermarkets and whether customers respond to such initiatives as intended. Studies show that health-promoting interventions in supermarkets can affect customers to purchase more healthy products [ 7 , 9 , 15 , 16 , 17 ]. Reviews and a meta-analysis have concluded that the most effective initiative in supermarket settings is price changes—the evidence points to the positive effects of reduced prices to increase the purchase of healthier products, especially fruit and vegetables [ 7 , 17 ]. Even though price reductions seem to be effective, they seem more challenging to implement due to retailers’ drive for profit and low preference for financing such price cuts [ 7 , 18 ]. There is some evidence that nudges in terms of product information and positioning, as well as altering the number of available products, can impact what products are being purchased [ 15 , 16 ]. However, the quality of this evidence is low. Overall, most of the studies that have explored the effect of interventions in supermarkets have been conducted in the USA and other high-income countries [ 15 , 16 ], in controlled settings, or applied a weak study design, such as non-randomised studies [ 16 , 17 ]. To our knowledge, only a few studies have been conducted in Denmark [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. These studies represent different designs and types of interventions: reformulation of private-label products to reduce calorie content [ 24 ], informational claims to promote low-salt foods [ 23 ], nudges via signs to promote sales of fruit and vegetables [ 22 ], positioning (shelf-space management) of dairy products [ 20 ], replacement of sugar confectionery with fruit and healthy snacks at the checkout [ 19 ], discount on fruit and vegetables combined with space management [ 25 ] and structural changes in supermarkets and education of supermarket employees as part of a multicomponent intervention [ 21 ] (the three latter studies are reporting from the same project). All but one study [ 23 ] found an effect of the applied intervention strategies, although mostly small or modest. This calls for more studies in real-life settings and investigations of why some interventions have the desired effect while others do not. Lack of effect may be explained by 1) customers not noticing or finding the initiatives relevant [ 19 , 23 ], 2) customers buying other products instead of or additionally to promoted intervention products [ 20 , 24 ], 3) the shelf organising effect [ 20 ], or 4) theory fail in regards to customer behaviour [ 22 ]. Several studies have explored facilitators and barriers to the implementation of health-promoting interventions in supermarkets. Reviews show that implementation is supported if the retailer is receptive to innovation, feels responsible for community health, and receives financial support or subsidies [ 26 ]. Furthermore, implementation is supported if the intervention provides the retailers with knowledge of health promotion and business skills [ 26 , 27 ]. Other facilitators include compatibility with context and customers’ needs, positive customer responses to the initiative, the prospect of improved public image, establishment of partnerships, low retailer effort requirements, and increased profit or sales [ 26 , 27 ]. Health-promoting interventions in supermarkets are hindered by high customer demand for unhealthy products and lower demand for healthy products, constraints of store infrastructure, challenges in product supply, high staff turnover, and lack of time [ 26 , 27 ]. Other barriers are doubt regarding changing customers’ behaviour, poor communication between collaborators [ 26 ], high running costs, and risk of spoilage [ 26 , 27 ]. Middle et al. [ 26 ] conclude that the underlying mechanism of barriers and facilitators of implementation is the (mis)alignment of retailers’ and intervention researchers’ interests. The authors, therefore, suggest a close collaboration between intervention researchers and retailers to work towards an alignment of interests and resolving or avoiding misalignment, which is supported by Gupta et al. [ 27 ]. However, knowledge of how such collaborative efforts affect the implementation of healthy food retail interventions is warranted. The aim of this study is to explore the implementation, acceptability, and feasibility of four different health-promoting food retail initiatives to increase customers’ purchase of healthy food and beverages, which were selected and developed together with food retailers: 1) Promotion of healthier breakfast cereals and products using shelf tags, 2) downsizing of sodas sold at the checkout desks, 3) downsizing of bags for the pick’n’ mix sweets, 4) replacement of a complimentary bun for children with a banana. The study has three research objectives: To document the implementation and sustainment of the initiatives over time To explore the retailers’ and customers’ responses to and acceptability of the initiatives To investigate barriers and facilitators of implementation and sustainment of the initiatives. Setting and the initiatives This study was conducted in Denmark during 2020 and 2021, 2 years that involved two major societal events, first the coronavirus disease pandemic and later the start of the Russia-Ukraine war. Both events heavily influenced the circumstances of everyday life including opportunities for conducting research and running businesses. The specific influences on this study will be unfolded later in the findings and discussion sections. In this study, we collaborated with the retailer Salling Group, which holds 34.2% of the market share of grocery retailers in Denmark [ 28 ]. Salling Group is owned by the Salling Foundations and has no shareholders—all profits go to reinvestment in the business and donations to sports (amateur and professional), charity, education, and research. Salling Group owns three national supermarket chains: føtex, Netto and Bilka, alongside other businesses. For the feasibility test, we collaborated with føtex, which owns over 100 stores all over Denmark, including 23 stores called føtex food. føtex (except føtex food) offers both groceries and many different non-food products (e.g. textiles, cosmetics, toys, electronics, and home accessories). The initiatives were selected and developed by a partnership, including a group of researchers at the National Institute of Public Health, University of Southern Denmark, consultants from the Danish Cancer Society, and employees at the Corporate Social Responsibility (CSR) department in Salling Group, the marketing department at føtex, and two store managers (hereafter referred collectively to as ‘the retailers’) over approximately 2 years. The process involved in-person meetings, desk research (the use of existing material [ 29 ]), visits to the test store, and a prototype test of three suggested initiatives. The researchers initiated the collaboration and were responsible for designing the research study and data collection and analyses. The retailers hosted the site of the feasibility test, contributed to the selection and development of initiatives and co-managed the practical part of the study. The Danish Cancer Society was recruited by the research project to develop the initiatives. A detailed description of the collaboration and development process is reported elsewhere (Duus et al.  unpublished ). The feasibility test ended up including four initiatives: 1) Promotion of healthier breakfast cereals and products using shelf tags, 2) downsizing of soda sold at the checkout desks, 3) downsizing of bags for the pick’n’ mix sweets, 4) replacement of a complimentary bun for children with a banana (suggested by the retailers). The initiatives were based on a compromise between the willingness of the retailers and the interest and ideas of the remaining partners rather than on what the literature suggests are the most effective strategies (Duus et al.  unpublished ). Detailed descriptions of the initiatives and the rationale behind them are found in Table 1 . The prototype test showed that 1) It was important to have a sign informing the customers about the initiative that offered a free banana to children instead of the usual free bun to create a better understanding of the changed offer; 2) Promotional shelf tags needed weekly maintenance as some would fall off; 3) It was difficult to sustain an initiative promoting ready-to-serve salads and ready-to-cook vegetables next to different fresh meats, as it met resistance among the staff due to being an additional task and led to more product waste (Customers did not expect to find these products next to the meat and therefore might not notice them). The learnings from the prototype test led to modifications of the implementation plan and the discard of the latter initiative. The prototype test also made us aware of how quickly the selection of food offered and the layout of the store changed over time, which the researcher, therefore, paid extra attention to during subsequent data collection. Moreover, the researcher made sure to update the list of products that should have a shelf tag a few weeks before the implementation to include new products offered. The føtex marketing department developed a script to inform the staff at the test store about the feasibility test, explaining and showing each initiative and the aim of the study overall. This was sent to the store manager after being reviewed by the researchers. The store manager was responsible for informing all relevant staff about the implementation and maintenance of the initiatives. The føtex marketing department also made sure to inform the relevant suppliers. Employees at the test store and brand staff from a brewery (who stock the coolers at the check-out desks) implemented the initiatives in the store. The research group did not correct or maintain the initiatives in the store after they were launched; however, the researchers monitored it and reported back to the retailers, either at meetings or by email. Overall study design The four initiatives were implemented in the test store for 6 weeks (or longer if the store manager allowed it) starting in September 2021. A føtex store in central Copenhagen (the capital city of Denmark) was chosen as the test store. This decision was made for pragmatic reasons, as the research institute is based in Copenhagen, and based on Salling Group’s decision as it offered their new store layout, which all stores were in the process of being converted to (it was the same store as where the prototype test was conducted). We designed a qualitative study involving participant observations and interviews to evaluate the feasibility of the initiatives. The methods were designed to explore the partnership and collaboration (the aim of another publication [Duus et al. Unpublished ]), as well as the implementation of the initiatives [ 30 ]. In the design of this study, we were inspired by McGill et al.'s (2020) two-phase framework of qualitative process evaluation from a complex systems perspective. This framework suggests an evaluation that looks at changes over time, starting with phase 1, the static system description and hypothesis generation about how the system might change when the intervention is introduced, followed by phase 2, an adaptive evaluation approach to the system undergoing change which follows emerging findings [ 31 ]. Data collection In-store observations. During October and November 2020, we mapped the store layout and customer flow in the test store as part of the static system description. Over 3 weeks, three research assistants performed 12 participant observations of 1005 min in total. The observations followed an observation guide which covered 1) the physical setting (e.g. the layout, placement of products, signs, and pictures); 2) the people (e.g. who are the customers? Are people shopping alone or together with others? How do they move around the store? What are the staff doing?) and 3) short interviews with customers (if possible) about their shopping at the particular store, and their thoughts about the layout of the store. The research teams’ access to the store was approved by the store manager, and research assistants wore a key chain with a sign showing their name and affiliation during the observations. During this data collection period, it was made mandatory to use face masks in supermarkets due to the coronavirus disease pandemic. As the implementation was delayed to approximately 1 year after this static description was completed, one participant observation in the test store was performed at the end of August 2021, just before initiative implementation, to document any major changes in the store layout and selection. Key lessons from these observations about the test supermarket and customers’ behaviour in the store included knowledge on 1) the route around the store, 2) the different times spent at the store, 3) interactions with objects (e.g. products and phones), 4) interactions with children, 5) behaviour of the staff, and 6) sensory impression (Additional file 1). These lessons informed our following data generation and assisted in contextualising our analysis. The first author monitored the implementation process through participant observations of status meetings ( n  = 2) and correspondence via email and phone with the store manager and the contact person at føtex. In-store participant observations were conducted during and after the feasibility test period, September 2021–May 2022 ( n  = 25 ~ 1795 min in total; see Additional file 2). These observations focused on documenting the presence of the initiatives as well as customers’ and staff’s responses to the initiatives. Access to the store was once again approved by the store manager, and the researcher wore a key chain. During the participant observations in-store, we conducted informal interviews with customers (see Additional file 2 for examples of questions), which lasted a maximum of 5 min each. The first author would approach people and ask if they were interested in answering a brief question. She introduced herself by her first name, where she worked and explained she was doing a research project about shopping patterns. The participant observations were documented by taking notes and photos. Handwritten notes were digitalised and written down at the first chance after leaving the store. Qualitative interviews Between November 2021 and February 2023, the first author conducted four semi-structured interviews with retailers ( n  = 3) who had been involved in the study (Table 2 ) to explore their views on the initiatives and the implementation process. Interview guides were used in all interviews alongside different prompts (e.g. timelines and documents). Interview guides were tailored to each participant’s specific role and involvement in the development and implementation of the initiatives. Besides questions related to the initiatives and the implementation effort, the guides included questions about the informants’ background and motivation for the project (personally and professionally), their view on their role and scope for action (individually and organisationally) and their perception of the collaboration with the other organisations. After the participants’ consent was given verbally right before the interview, the interviews were recorded and later transcribed verbatim. To explore the level of implementation (research objective I), all field notes and photos taken during and after the feasibility test were reviewed to assess whether the initiatives were present and to what degree (e.g. x out of x possible tags). To explore the perception of the initiatives among employees and customers (research objective II) and identify barriers and facilitators for implementing the initiatives (research objective III), we followed a thematic analysis inspired by Braun and Clarke [ 32 ]. Firstly, field notes and interview transcripts were read thoroughly and openly coded, by writing keywords in the margin of the material, with a focus on the two research objectives. After initial coding, the codes were summarised into broader themes, by writing them into a document with short descriptions and revised according to data excerpts and the full empirical material. The themes drew on the process evaluation concepts: acceptability, responsiveness [ 30 ], motivation, general capacity to implement [ 33 ] and commercial viability [ 34 ]. Lastly, the themes were named, and the final analysis was written up. We have structured the presentation of study findings as follows: Firstly, we present the implementation of the initiatives overall. Secondly, we present the implementation of each initiative, customers’ responses to them, and the retailers’ perspectives. Lastly, we present the overall facilitators and barriers to the implementation of the initiatives. Implementation of the initiatives The implementation of the initiatives was challenged. Firstly, we found that not all the preparations for the implementation were finished in time for the scheduled day. On the scheduled day, the retailer decided to push back the implementation by 1 week. The main reasons were that there had been some misunderstandings around the ordering of the smaller sodas. It was informed that the smaller soda would be a 330 ml can instead of the 375 ml bottle at the price of DKK 10.00 (~ 1.3 euros). The 500 ml bottle usually sold at the coolers cost DKK 16.00 (~ 2.2 euros). The Danish Cancer Society and the research group had two concerns about this: 1) the use of a can instead of a bottle would make the interpretation of the results very difficult, as the bottle and the can have two different functions to the customer—with the can, the product would be consumed all at once, whereas the bottle with the screw lid could be saved for later after it had been open; 2) the price was too low—the price per litre would be lower on the smaller sodas than it had been on those replaced. No changes were made despite these concerns. Secondly, just days before the implementation, the retailers informed the other partners that they would stick with cans for the test of smaller-sized sodas and that they would now be 250 ml. They acknowledged that both the size and the packing were not optimal but that the optimal 375 ml in a bottle was just not possible. Additionally, they informed the researchers that they could no longer find the new bags produced for the pick’n’mix sweet display. These challenges led to a delay of the implementation of the initiatives by 1 week, but also a staggered implementation, where the initiatives were implemented when ready (the soda initiative 2 weeks later and the bags for pick’n’ mix sweets 8 weeks later). The retailers agreed to push back the end day correspondingly, upholding the 6 weeks of implementation. Table 3 shows an overview of the implementation of the four initiatives according to the day and week of the feasibility test period. Smaller product sizes of sodas at the checkout desk As seen from Table  3 , we did observe the implementation of a smaller product size of the targeted sodas in all coolers, besides the one at the bakery, in the week leading up to the agreed date. We hereafter observed a full implementation of 250 ml cans during the first 2 weeks of implementation. During the third week and the beginning of the fourth week, we observed a mix of 250 and 330 ml cans or only 330 ml cans. The store manager explained that this was probably due to non-delivering from the supplier. At the end of the fourth week and for the last 2 weeks, we observed a full implementation of 250 ml cans. As the targeted size of the initiative was a 375 ml bottle, the initiative was not implemented as intended. After the 6-week feasibility test period, we observed that the smaller 250 ml cans were available in all coolers for at least eight more weeks. As expected, the presentation of the coolers fluctuated over the period. On days of stocking (Monday, Wednesday, and Friday), the coolers would look neat and full, while they would appear more empty or messy on other days. Customer responsiveness We observed very few customers who bought any products from the coolers, and we did not get to talk to any customers about the initiative. However, the observations in the store showed no distinct change in customers’ behaviour around the coolers nor expressions of discontent or excitement with the initiative. In an interview with the store manager, he explained that he believed customers had not noticed the change. Retailer perspectives The store manager was positive about the initiative, but from his perspective, the decision to implement it should be made at the procurement level and by the suppliers. However, he did have an opinion on how to implement it. The price needed to be fair according to the product it replaced. Moreover, he drew attention to the fact that it was the supplier’s personnel who stocked the products rather than his own. The store manager was, therefore, not surprised that the employees at the store had little to say about the initiative. føtex’s representative (B) was also positive about the initiative and expressed in the interview that the chain would be willing to implement it—if they found it to be the ‘right thing’ to do. However, the representative also emphasised the importance of agreeing with the suppliers, which is a time-consuming process and ‘not done in just six months’. Shelf tags for breakfast cereal products From the first day of the implementation, some tags were missing, and one tag was consistently misplaced (Table  3 ). During the first 3 weeks, 10% ( n  = 3) of the tags were missing. This portion progressively increased to 23% until the end of the fifth week. In the sixth week, the portion decreased at first to 16% but decreased again and ended at 26%. In the weeks after the implementation period, the tags stayed present but slowly came off. Approximately 6 months later, three (10%) of the tags were still present. We observed throughout the feasibility test that the presentation of the area varied, which is to be expected in a busy supermarket. At times, the area looked messy; boxes would block access to some products, products would be sold out, some would change packaging, and new products would be introduced to the selection. When we asked customers about the tags, we learned that they had been unaware of them and that some believed that it was not something they would use—some did not know the meaning of the labels on the tags, while others did not find the labels relevant for them. [The tags] don’t matter. My wife is pretty health conscious, so we don’t use those, let alone know with such a thing as breakfast cereal. (Male customer) From our observations of the behaviour of the customers in the breakfast products and cereals department, we find two interesting groups: Those who shop alone and those who shop together with others (primarily children). These groups seem to practice different behaviours. Among those who do their grocery shopping by themselves, we find two subgroups: 1) those who have planned or know exactly what they want to buy, and 2) those who decide at the store. For the first sub-group, we observed that some showed this by practising a behaviour where they would walk quickly and purposefully towards the shelves and quickly pick up a product. Others would look determined to find a specific product, as the fieldnote excerpt illustrates: A woman stands looking at the muesli. She first grabs an orange bag on the bottom shelf, then a more yellow one next door and puts the first one back on the shelf. She inspects the bag she took. She starts to look around the shelves more and reaches for a bag that has a pinker look on the top shelf. She puts it back and reaches into the space next to it, where there are a few bags at the very back, but she has difficulty reaching them. A man comes by, notices the woman, and offers to help her. The woman indicates a yes, and the man reaches up and grabs a bag ‘that's the one!’ says the woman as the man hands her the bag. Another example was a man who kept looking back and forth between some muesli and granola products and his phone before he eventually chose a product. It is unknown whether the man was looking at a specific note, a text request from his family, or a picture on his phone, yet what was on his phone seemed to determine the product he bought. Overall, this group seemed very unlikely to be influenced by the tags, as they had made their choice already before they entered the store. For the second sub-group, those who seemed to make their decision in the store, we observed that some would just stop and glance at the products without choosing one before moving on with their shopping. Others would look more randomly at the selection than those described above, walk back and forth in the aisle, compare different products and read the info on the back of the products. For those who shopped together with others (most often children), we observed that when adults shopped with children, the choices of the child and the choices of the adult often conflicted. In one example of a child and a woman who looked at breakfast cereal products, the child was initially allowed to pick a product and asked for different chocolate variants, which all featured cartoon figures; however, the woman rejected all of the child’s choices. In the interaction, the child was met with demands from the woman regarding the attributes of the products: they could not contain chocolate or sugar. In the end, it was the woman who chose a product based on her experience of the child’s preferences and her criteria. In similar situations, we did observe an attempt at compromising between the adult’s and the child’s criteria, which was explained by this woman: I ask them [woman and boy aged about 10] what they look for when choosing breakfast cereals. The woman looks at the boy and says, ‘Well, what are we looking for?’. The boy does not answer but looks at her and me and smiles. The woman herself replies, ‘Something we can agree on. Something he likes but is not too unhealthy, either’. I ask her what she considers unhealthy. She waffles for a bit and then replies, ‘Yes, but he wants that Lions cereal, for example, and I don’t want him to have that. So something that’s not de facto sweets’. She takes the box of granola that they have chosen [Paulún's blueberry/lemon granola] out of the basket, looks at it and says, ‘So we chose this one. There's probably also a lot of fructose and caramelised stuff in it, but yeah.’ This illustrates the high impact children had on the choices of breakfast products, but also how the parents tried to control and negotiate the final choice. Retailer perspective The store manager had little faith in the effectiveness of the shelf tags: The thing about tagging cereals, I don't think that makes the slightest difference. The reason why I’m sceptical in that regard is that it’s a mixture of what I do on a daily basis. It’s especially the behavioural patterns of our customers, but also how I act as a customer myself to a degree. I don't think shelf tags with the whole grain label or anything like that; in my experience it hasn’t changed things much. (Store manager) His view on the effect of the initiative was in line with our observations of the customers in the store. Furthermore, the store manager explained that it was difficult to maintain the initiative, as it was not part of the employees’ daily routine. This was also the argument of why the tags lingered after the test period—it was just not part of the usual protocol either to hang them up or take them down. This perspective was shared by the føtex representative (B), who also highlighted the cost of this maintenance. Contrary to the store managers’ sceptics, the føtex representative (B) was more positive about the initiative: I think it’s a good initiative. We work a lot with tags and labels in general. [...] I think making it transparent to the consumer is really interesting because there’s nothing wrong with buying a box of Nesquick cereal every once in a while. At least we should not claim it’s the wrong thing to do. But you just have to be clear about what you’re buying, and I think those labels help with that. (føtex representative (B)) She explained that the initiative was highly compatible with their usual strategies. However, she also explained in the interview that a barrier to using shelf tags to promote the buying of certain products was that the chain was trying to reduce the printed material they used in their stores as part of their CSR strategy and to reduce costs. Replacement of the complimentary bun for children with a banana The complimentary banana was fully implemented in the feasibility test period except for 1 day of observation, where the signs were not visible (Table  3 ). The initiative also remained available and present by the sign for at least 10 weeks after the implementation period. Furthermore, the store manager informed the researcher that they would continue to provide bananas for customers requesting this as an act of customer service. From the observations, we do find that the presentation of the initiative changed throughout the period. At first, the bananas were placed in a cardboard box on the display counter, which was later replaced with a nicer-looking basket. The number of bananas and their colour also fluctuated during the different days, which would be expected due to the delivery of the bananas and how often they are restocked. However, compared to the buns, we never observed that the bananas were not available, making it a reliable offer no matter the time of the day. We observed two ways (1 and 2) that the complimentary offer for children was brought up: 1) A customer would ask for the ‘bun for children’, or 2) the staff would offer the complimentary banana to buying customers. In the first way 1), we saw two responses from the staff (a and b) and the customers (i and ii): (a) The customer would be offered the bun with no mention of the banana, or (b) the staff would inform the customer that they no longer offered buns but that they offered a banana instead. The customers had two primary responses to this message: (i) The customer rejected the offer and decided to buy a bun or another item instead. The child was often included in this decision. (ii) The customer accepted the offer and received the banana. In some cases, the child did not accept the offer and the customer compensated for this response by buying a bun or another product for the child. In the second way 2), in which the staff offered the banana spontaneously, the customers almost always reacted positively and accepted the offer. The following excerpt illustrates why some customers rejected the offer: A woman with a child of about 1-year-old in a stroller walks up to the bakery and asks for a children's bun. The child has already noticed the buns from the moment they arrive and sits, pointing at the buns through the glass window and babbling. The shop assistant says that there are no children's buns but bananas and points to the sign. The woman replies, ‘I’d like to buy a bun, then’. The assistant takes the bun and enters it into the till, while the woman says, ‘Bananas are so messy’. The assistant smiles and says, ‘Well yeah, I'll pass that on’. The woman replies, ‘It's just that the banana is rather a bother, and the assistant replies, ‘But I think we’ll be offering [the buns] again eventually’. Thus, adults rejected the offer because eating a banana was a messier process than eating a bun. During meetings and interviews, the retailer also highlighted this as the main reason for rejections of the offer, especially among those with younger children. Another reason for rejection was that the parents did not appreciate the offer nor perceived a need to offer their children a banana instead of a bun. This initiative was the most successful and interesting one in the eyes of the store manager. I’d like to highlight the banana for kids, which is clearly the initiative I found most customers were pleased with. (Store manager) Many customers responded positively to the new offer, which was emphasised as a marker of success. It was also the reason why the initiative continued after the 6-week period, and the store manager explained that they would continue to give bananas to those who asked for them. The following excerpt illustrates what the bun meant to føtex and the chain’s relationship with its customers. The children's bun has been around for donkey’s years, and it’s become ingrained in parents and kids alike that you can get them in føtex. So, we’re quite interested in learning how many people would actually, if presented with the alternative, choose something else, like, for example, the banana. I’m quite surprised by that – we can't track it, unfortunately – but off the top of my head, up to 40 to 50 percent actually choose the banana. I find that very interesting. (føtex representative (B)) Thus, it came as a surprise that the initiative was so well received. However, despite the positive experiences with the initiative, the retailers also commented on the cost. They highlighted that the banana was more expensive than the bun, and if it should be an option offered in all stores, then it would have to be prioritised at the executive level as an additional expenditure. In this case, the banana would only be an alternative to the bun and not a replacement. This was rationalised by the retailers’ attitude of not making choices on behalf of the customers. Smaller bags for pick’n’ mix sweets This initiative was not implemented until 8 weeks after the initial implementation date. It was fully implemented for five out of the six weeks; during the third week, we observed that the old, larger bags had been hung in front of the new smaller bags. At 2 weeks and four and a half months after the feasibility test, the smaller bags could still be found behind the larger bags—however, it is unlikely that these would have been used, as the obvious choice would have been the bag at the very front. As described for the other areas, this also fluctuated in its presentation and stocking. We did not get any direct reactions from customers on the smaller bag. However, our observations showed that different strategies were used to decide the amount of candy among customers who bought pick’n’mix sweets. Some showed signs of visually assessing the amount of sweets in the bag, which were the customers we would expect to influence. We often observed this strategy among adults with children, where it was the adult who would visually assess the amount and communicate to the child when they had picked enough. Those with very young children would walk alongside the child and select the sweets for them, and some adults would encourage the choice of the child by pointing out different variants and commenting on the appearance of the sweets. Other strategies were to mix according to a pre-defined number of pieces or volume: A boy of about 10 and a girl of about 8 come over and mix sweets. They repeatedly weigh the bag while doing so. A woman comes over, and the girl says, ‘Hello mummy!’ The woman says, ‘Don’t forget to weigh it’. She then grabs a bag herself and begins to mix sweets. The boy asks the girl, ‘Did you weigh it?’. The girl walks over to the scales and says, ‘I think I’ve got enough’. However, she does not close the bag, and she begins to walk around somewhat restlessly, then says, ‘I don’t know what to pick. I’m still [a few] grammes short’. An interesting aspect of the situation above is that the girl expressed that she was satisfied with what she had chosen, but she felt that she had to meet the prespecified weight and, therefore, tried to find more sweets to put in her bag. Such strategies undermine the mechanism which the initiative was trying to influence. Overall, the retailers were positive about this initiative. The føtex representative (B) highlighted that this initiative was interesting as it was a stealth initiative, compared to the initiatives with the sodas, and would change the behaviour of the customers without them noticing. In her opinion, this was not a problem, as people paid per gram. The store manager had a clear demand for the implementation; it should be easy for both the staff and customers to use. This perspective was backed up by a føtex representative (B) who said: If there’s something that doesn’t work for us, it’s... if it doesn’t work for our customers, that’s what we need to solve first. (føtex representative (B)) This shows how one success criterion of the retailers is customer satisfaction, which we elaborate on later (See: Influence of customers and other stakeholders on store operation). The initiative was very delayed, and one reason was that it was challenging to create a new bag that would work in the store. This resulted in the order of many different bags in large quantities due to the agreements with the suppliers, which had been very costly for the retailer. The føtex representative (B) also reflected on what the potential evidence of an effect would mean to the retailer: Then we’ll have to wait and see if people buy fewer sweets. And of course, this is something that we must take into account because it’s no secret that part of being a responsible business is to make a profit. And if we sell fewer sweets, then we make less money. (føtex representative (B)) This shows how health and financial profit were seen as opposites and how the success of the initiative would not necessarily lead to it being viewed favourably, as it would negatively affect their profit. Any implementation in the chain would, therefore, have to be a strategic decision. Facilitators and barriers In the sections above, we have focused on the four specific initiatives. In the following, we will present analytical findings that go across the initiatives and elucidate what facilitated and hampered the implementation of the initiatives overall. We have organised our findings under three headings: Health is not the number one priority; General capacity of the retailer; and Influence of customers and other stakeholders on store operation. Health is not the number one priority In this section, we present the retailers’ motivation for and interest in engaging in the project and working with health and health promotion and what drives and/or curbs this motivation. In our understanding of motivation, we draw on Scaccia et al. [ 33 ] and view motivation as incentives and disincentives that contribute to the desirability of using an initiative focusing on health. We find that the retailers expressed motivation for working with health and health promotion, which at first seemed to be based on interest. The retailer representatives explained how they personally were interested in health and wanted to learn more, but also that the organisation had an interest in health, especially among children and young people, and wanted to contribute to health-preventing activities, for example, by financially supporting local sports clubs. According to one retailer representative, this was because physical activity and healthy eating promote happier customers, as well as happy employees. The argument points to retailers’ focus on customer satisfaction (see: Influence of customers and other stakeholders on store operation). The focus on the customers relates to another factor of motivation: Working with health was also seen as a relative advantage in that customers increasingly demand healthier products and alternatives. Lastly, we found that the motivation for working with health was a feeling of obligation due to the view of having a social responsibility: I would say, in purely business and commercial terms, we are, indeed, a commercial business that was created to make money. There’s no ignoring that (laughs). So, of course, this is our main KPI [key performance indicator]. But that being said, we also agree that we have a social responsibility because we are as big as we are. We make a lot of foodstuffs available to the Danes, as do many of our colleagues in our industry, so there is no doubt that we have a role to play in terms of what we make available. (føtex representative (A)) According to the excerpt, this obligation was rooted in the size of the organisation and, thereby, the major influence on people’s selection of food products. However, the excerpt also highlighted that health was not their first priority, which was profit. This point has been repeatedly mentioned among retailers, which reinforces its validity; they were a business and had to gain profit to keep running their operation, which presented limits for what could be implemented. The store manager even expressed how he perceived the running of a supermarket and promotion of public health as incompatible goals and something he had never seen an example of in a real-life supermarket. However, from the interviews with the retailers and our fieldwork, it seemed that this was not completely black and white, as the retailers were willing to give up their profit in some cases. An example is the hiding of tobacco products in all Salling Groups’ supermarket chains, which they voluntarily implemented in 2018, which led to a significant decrease in profit from tobacco products. After all, the Salling Group pioneered this with tobacco products. I'm proud of that, but I also think it’s the right thing to do. My personal opinion is that it was the absolutely correct move they chose to make, by making it harder to market a product that is obviously bad for my health. We’re not there with pick‘n’mix sweets just yet, in that we would claim they’re bad for your health, but the mindset in terms of; that is, upholding the mindset when it comes to cigarettes is something that we, as an industry, can easily support in close cooperation with, among others, yourselves [researchers] and the industry. (Store manager) Risk seemed to be the driver. If the retailer was convinced that the risk was real or big enough, then they were willing to give up some of their profits because it was the ‘right thing to do’, and they would have the courage and power to do so. It was mentioned by all three informants that they did not believe in bans, limitations or hiding of products, as this interfered with the customer’s freedom of choice. This viewpoint was a barrier to the implementation of all initiatives that used strategies that would minimise or reduce the availability of a product. Yet, as with the tobacco products, we found other examples where this restriction of choice was justified by the retailer. One example was that the føtex chain only sold organic bananas. From a sign in the store, this was because: ‘we want to avoid the spray agent chlorpyrifos. Among other things, it is suspected of harming the development of children and foetuses. We can’t live with that suspicion and therefore you can only buy organic bananas in the future’ As with the cigarettes, the argument here was the health risks. In the interview with the store manager about restricting choices, animal welfare and political reasons (e.g. Russia’s warfare against Ukraine) were mentioned as other arguments for doing so. So, despite an immediate motivation for working with health, the retailer also expressed how other interests and priorities may hinder and set aside the work with health. General capacity of the retailer This section presents our findings relating to the general capacity of the retailer in the form of resources, organisational size, and culture. General capacity is understood as the readiness or ability to implement any new initiative [ 33 ]. Through the interviews with the føtex representative (B) and working together with the retailer during the project, we have found that the retailer seemed to be used to and willing to implement new initiatives. In this current study, they accounted for all expenses related to the development of materials for the test and were also willing to risk some of their profit for a short period of time. The føtex representative (B) highlighted this high level of available resources several times in the interview: I have some leverage, so when we do something, we don’t do it by halves. What I find most motivating, and I can say that with complete peace of mind, is that if the Salling Group says they’re going to do something, or if føtex says they’re going to do something or says they want to win this particular battle, then we win it, and then we do it to the full. [...] So when we say, for example, with this health project, that ‘we want to work with health,’ then we do want to work with health, and we’re going to make a difference in health, too. (føtex representative (B)) In this excerpt, she expressed that the mere size of the company allowed them to push any agenda if they wanted to. However, this also underlines that this capacity is dependent on the retailer’s willingness, a willingness that was not in favour of many of the initiatives that the researcher, based on the literature, thought would have the greatest effect. Even though the size of the company came with many available resources, the retailer also explained how the size of the company had worked against the project in several ways: What I think made it difficult for us to get through with some of these things let's just take the sodas, in that case, we have a private label collaborator who has production facilities, and when they press the ‘Salling sodas’ button, it doesn't just spew out a few thousand bottles, but millions. So saying ‘can't we just try to reduce the size and give it a try.’ It's a giant setup, so it’s not possible to do that at a whim. You’d need to get a whole or half chain on board that can help sell such volumes because otherwise, the costs would go through the roof. (føtex representative (A)) What this excerpt explains is that even changes that appeared small would take tremendous effort and be very costly, due to the size of the organisation. Another challenge of the implementation was embedded in the retailers’ organisational culture. Føtex representative (B) explained in the interview that conflicting goals between employees made it difficult and time-consuming when implementing new initiatives. Another barrier to implementing the initiatives was high staff turnover at the retailer. In an interview with a føtex representative, she explained that people often shifted around different positions in the organisation, which ended in the project falling between two stools, leading to misunderstandings of agreements and changes in attitudes towards the initiatives. In summary, we find that the retailers could, in some respects, have a strong general capacity to implement new initiatives by having available resources and being used to implement new initiatives. Regardless, this study shows that this was not utilised due to a lack of willingness. Moreover, we find that the size and organisational culture of the retailer hampered the implementation of the initiatives. Influence of customers and other stakeholders on store operation The last section reports on the influence of customers on the retailer’s willingness to implement the initiatives, and the influence of other stakeholders, especially producers, on what can be implemented. We found that the customer’s reactions and attitudes were determining to the retailer when implementing any new initiative, as indicated in the sections above. According to the retailer, the customer was the focus when designing the layout of the store: We are in very close dialogue with our clients, we do quantitative surveys and we do focus groups, we do in-depth interviews. And in that context, we're trying to understand, when you're shopping, how do you go about it. Is it easy for you to find the items you are looking for? And based on the responses, we try to adapt our stores to make things easy for our customers. (føtex representative (A)) The same representative also mentioned that she thought it would have been a strength of the project to have conducted interviews with the customers as a part of the development process, emphasising the weight they put on the customer’s attitudes. The retailers highlighted the importance of customer satisfaction and convenience in their shopping experience as a barrier to implementing certain initiatives, such as changing the placement of products. However, these same factors have also proven to be facilitators for other initiatives, such as the tags for breakfast products and the complimentary banana for children, as demonstrated above. Another important stakeholder for the supermarkets was the suppliers of their products. Others were government actors (e.g. the Danish Veterinary and Food Administration). For both downsizing initiatives, the suppliers of the products (sodas and bags for sweets) were key to the success of their implementation. In an interview with the store manager, he explained the huge role some of these suppliers have in the daily operation of the store and the chain. After all, we’ve got a chain agreement that our head office has made with the breweries. I don’t get to decide which items are in our refrigerators. [...] The tricky thing is that we’re not only dealing with føtex or the Salling Group. We also have to do with some other, equally large companies that are also just coming in. Plus, I have people here X times a week to service their particular area. [...] [Another thing] that proved tricky, as far as I recall, was that the alternatives offered, people felt strongly about those because the breweries made some strategic choices, and because of those, some of the items that we might be able to stock, they didn't want to sell those. (Store manager) This excerpt illustrates how suppliers like the breweries, as shown earlier, influenced the implementation and affected the decisions made by the retailer. This section indicates that even though the retailer is convinced that a given initiative would be interesting to implement in their supermarket, the suppliers often must agree as well, and finally, the customers must also welcome it. In this study, we have explored the implementation, acceptability, and feasibility of four different health-promoting food retail initiatives aimed at customers in a real-life supermarket setting, using different qualitative methods. We found that (i) Two initiatives (downsizing of bags for the pick’n’ mix sweets and the complimentary banana for children) were implemented to a high degree, yet delivery issues caused delays according to the planned date, especially for the bags. The downsizing of soda bottles was not implemented as intended; the size and packaging deviated from the original plan due to delivery failure. Moreover, the implementation decreased over the feasibility test for the initiative with shelf tags, as it took more continuous maintenance. For all initiatives, we found that they lingered after the feasibility test; however, only the banana for children was somewhat sustained for a period to accommodate customer demand. (ii) The retailers expressed different levels of acceptability towards the initiatives, and different representatives sometimes also showed different levels of acceptability towards the same initiative, such as the tags on the breakfast products. The most well-received initiative was the banana for children, which is somewhat unsurprising, as it was the retailers themselves that suggested including this initiative. Additionally, the positive response from the customers that they got supported the retailers’ positive attitude towards the initiative. We also found that many customers responded well to this initiative; however, we also observed a group that did not accept the initiative and preferred the bun over the banana. For the remaining initiatives, customers did not seem to notice them. Yet, we did observe customer behaviours that would probably work against the suggested mechanisms of some of the initiatives. (iii) In general, we describe three themes of barriers and facilitators that influence the implementation and possible sustainment of the initiatives: Health is not the number one priority, General capacity of the retailer, and Influence of customers and other stakeholders on store operation. Firstly, we found the retailers were motivated to work with health, both from a personal and professional perspective. The motivation was rooted in a feeling of social responsibility as well as health initiatives being viewed as a relative advantage, due to demand and making customers happier. Still, other priorities, such as profit and maintaining customers’ ‘free choice’, challenged the motivation to implement such initiatives. Secondly, the retailer showed a high level of available resources, which supported their general capacity to implement the initiatives; however, the large size of the organisation and its culture proved to be barriers to the implementation. Lastly, the analysis showed that the influence of both customers and other stakeholders was crucial to the implementation, both in terms of what is possible and what the retailers would be interested in and prioritise. Our findings are similar to those of others [ 26 , 35 ]. Winkler et al. [ 35 ] found that even though supermarket actors found health-promoting initiatives meaningful to engage in, their engagement was challenged by a business mindset, practical routines, and structural requirements. Thus, despite the involvement of retailers in the development, selection and implementation of the initiatives, studies suggest that healthy food retail initiatives still encounter some fundamental barriers towards the implementation, such as the economical aspect or the view on customers’ free choice. However, our results also indicate that it might be possible to persuade food retailers to remove products or restrict choices if the evidence or arguments of it being the right thing to do are sufficiently strong, as with organic bananas or tobacco products. This has also been the case of another retailer in Denmark, which has decided that all their stores should be tobacco and nicotine-free by the end of 2028 to reduce the number of smokers [ 36 ]. Another solution is to identify win–win initiatives, as the complimentary banana for children was somewhat an example of (if we consider the banana as a healthier alternative) and which other studies have found as well [ 35 , 37 ]. Even though the four initiatives were implemented (yet two not as intended) in this study, and we found them to be somewhat acceptable to the retailers, we must still highlight that these initiatives represent a very small portion of the initiatives first suggested and entail several compromises from what the researchers had initially planned (Duus et al. Unpublished ). Moreover, the customer’s responses to the initiatives were mixed, and in some cases, their behaviour indicated that the initiatives would have little effect. Compared with studies testing similar initiatives, we find that 1) Shelf tags alone were found unlikely to change food purchases [ 38 ] and are likely to contribute to disparities in food purchases as not all customers know nutrition labels or have the literacy to read and understand them [ 39 ]. 2) Smaller bags for pick’n’ mix sweets could be successfully implemented and, based on results from another study, might be able to decrease the volume of sweets sold [ 40 ]. Moreover, others have also shown that customers are willing to buy smaller product options [ 41 ]. Taken together, this suggests that voluntary engagement with researchers might not suffice to make changes that would improve the supermarket environment as opted for to support population health. This view has also been suggested by Winkler et al. [ 35 ], and in the Lancet series on commercial determinants of health, an even more critical perspective on engagements with commercial actors as food retailers is presented [ 42 , 43 ]. Here they warn against how commercial actors use partnerships with researchers, among others, as a tool to improve their reputation and credibility [ 42 ]. In our collaborative process with the retailer, we experienced many challenges. We did not accomplish aligning retailers’ and researchers’ interests as scholars have suggested being the prerequisite of implementing healthy food retail interventions in supermarkets [ 26 , 27 ]. This underlines the importance of the pre-intervention phase, as described by Hawe, Shiell, and Riley [ 44 ], which is fundamental to a successful implementation. During the pre-intervention phase, the establishment of relationships between different people or agencies often occurs, and these relationships may play a crucial role in the implementation and the explanation of why some work and others do not [ 44 ]. In line with this, another study has suggested exploring what implementation strategies might promote the uptake of evidence-based interventions among food retailers [ 45 ]. They found that contrary to many other studies, the intervention in their study was compatible with the interest of the store managers to which it was presented—these store managers had a strong feeling of social responsibility towards the communities they operated in [ 45 ]. Strength and limitations The investigation of the feasibility test was strengthened by using different methods, process evaluation concepts, and a broad view including both the delivery and presentation of the initiatives as well as customer and retailer perspectives. We primarily got the retailer perspective from a strategic level, yet we had planned on conducting focus group interviews with staff at the test store to get perspectives from an operational level on the initiatives and the implementation process. However, no staff wanted to participate in an interview. The store manager explained that this probably was due to three things: 1) They had no interest in the study, or they were tired of the study, 2) the recruitment was done too late (approximately 2 months after the feasibility test period), and 3) the staff was overworked as a result of understaffing due to the coronavirus disease pandemic. Future studies aim also to analyse sales data in order to evaluate whether any changes in sales of the products we intervened on occurred. However, with the available data, we will not be able to analyse whether the initiatives change people’s eating patterns or whether they influence people differently in terms of their socioeconomic factors or other characteristics. A thorough needs assessment [ 46 ] among supermarket customers to test the initiative’s assumptions and their food purchase patterns would have strengthened the study. However, this was not possible within the timeframe and funding scheme, so the development drew primarily on existing knowledge and the experience of the retailer and the Danish Cancer Society. Furthermore, the store visits conducted in the store during the development of the initiative also provided a few customer perspectives, which led to the exclusion of some ideas (Duus et al.  unpublished ). Furthermore, we learned two methodological lessons from the in-store observations: 1) All observers were met by the feeling of being ‘in the way’ and a need to be in almost constant movement to not interfere with the order in the store. The observers were met with a feeling of self-awareness and a need to legitimise their presence at the store by wearing a sticker on their shirts saying ‘visitor’ or their university identification card. These feelings were amplified by the governmental advice of social distancing and the requirement to wear face masks in grocery stores, introduced during the period of observations. 2) Concerning this, the observers also found it challenging to approach customers for the short interviews due to the feeling of invading people’s private space, hence only five were conducted. This was especially challenging when wearing face masks, as it was impossible to produce and read non-verbal signals (e.g. smiles), and difficult to hear what people were saying. Implications for future studies and practice This study presents an investigation of the implementation of healthy food retail initiatives for supermarkets that have been developed and selected together with retailers as suggested by the literature. It suggests that the implementation of such initiatives is possible and—to some degree—high. Yet, the quality of the initiatives was rather low, and some were not implemented as intended. Moreover, we still present some of the same barriers and limitations as former studies that have not implemented collaborative strategies in the pre-intervention phase. Some of this may be due to challenges such as a high staff turnover at the retailer and a lack of a shared understanding, as shown in another study (Duus et al. unpublished ). Future studies must explore this further. Lessons for future studies are to identify initiatives that customers appreciate, as this is important to retailers. Underlining a needs assessment as an important first step in intervention development [ 30 , 46 ]. Furthermore, future studies should involve a broader range of stakeholders, including manufacturers and suppliers, in the development of the initiatives, as they have significant power over what can be implemented. Future studies would also benefit from identifying and testing implementation strategies that can facilitate the implementation of this type of intervention in this setting. We performed a qualitative investigation of the implementation, acceptability, and feasibility of four different healthy food retail initiatives aimed at customers in a real-life supermarket setting, which had been developed and selected together with retailers. Only two of the four initiatives were implemented as intended, and the perspectives of retailers and customers were mixed or unclear. Altogether, the study highlights the challenges of implementing healthy retail food initiatives despite early involvement of retailers in the selection and design of those initiatives. Adding to the challenges of implementation, the initiatives also represent a compromise between the interests of the researcher and the retailers and do not represent what the literature suggests as the most effective strategies. A compromise made to uphold the partnership and complete the funded research project. Future studies should further examine the impact and pitfalls of including retailers (or other commercial actors) in the development and selection of healthy food retail initiatives and try to identify successful implementation strategies facilitating implementation. 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Download references Acknowledgements We want to thank all the participating retail group and supermarket staff members involved in this project and the implementation process. We appreciate the time and effort you have dedicated to this project and your openness. Furthermore, we want to acknowledge the customers who took the time to share their opinions with us during their daily grocery shopping. We acknowledge Johanne Aviaja Rosing, Louise Ayoe Sparvath Brautsch, and Carl Johannes Middelboe for their assistance in conducting the pre- and post-intervention observations. Open access funding provided by University of Southern Denmark This study is funded by the Danish Cancer Society, grant no.: R274-A16920. The first author (Katrine Sidenius Duus) has also received a Faculty Scholarship from the Faculty of Health Sciences at the University of Southern Denmark to support the completion of her PhD thesis, which this study is part of. Author information Authors and affiliations. The National Institute of Public Health, University of Southern Denmark, Copenhagen, Denmark Katrine Sidenius Duus, Tine Tjørnhøj-Thomsen & Rikke Fredenslund Krølner You can also search for this author in PubMed   Google Scholar Contributions KSD, RFK, and TTT contributed to the funding acquisition, study conception and design. Data generation and analyses were performed by KSD. The first draft of the manuscript was written by KSD. RFK and TTT commented on previous versions of the manuscript and contributed in writing the final manuscript. KSD wrote up the final manuscript. All authors read and approved the final manuscript. Corresponding author Correspondence to Katrine Sidenius Duus . Ethics declarations Ethics approval and consent to participate. This study has been approved by SDU Research & Innovation Organization (notification no. 11.136). All informants who participated in interviews received written and verbal information about the aim, that participation was voluntary and that their information would be used for research purposes only and treated with confidentiality. By participating, consent for their data to be used for research was given. Data from the observation and documents were handled confidentially and with caution to protect sensitive information that could identify individuals. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Additional information Publisher’s note. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information Supplementary material 1., supplementary material 2., rights and permissions. 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The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Reprints and permissions About this article Cite this article. Duus, K.S., Tjørnhøj-Thomsen, T. & Krølner, R.F. Implementation of health-promoting retail initiatives in the Healthier Choices in Supermarkets Study—qualitative perspectives from a feasibility study. BMC Med 22 , 349 (2024). https://doi.org/10.1186/s12916-024-03561-2 Download citation Received : 24 May 2024 Accepted : 14 August 2024 Published : 02 September 2024 DOI : https://doi.org/10.1186/s12916-024-03561-2 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 Implementation Qualitative research Health promotion Supermarkets Involvement Intervention BMC Medicine ISSN: 1741-7015 General enquiries: [email protected] 453 COMMENTS 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 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. Quantitative research collects numerical ... 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. Difference Between Qualitative and Qualitative Research 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. Qualitative vs Quantitative Research Qualitative v s Quantitative Research . Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality - characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. Qualitative Vs Quantitative Research Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the research process, how research questions are set up, how existing theories are built upon, what research methods are employed, and how the findings are unveiled to the readers. You may adopt conventional methods ... Qualitative Vs Quantitative Research It can help add a 'why' element to factual, objective data. Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyze than quantitative data. This qualitative data is called unstructured data by researchers. Qualitative vs. quantitative research The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Qualitative vs Quantitative Research 101 This is an important cornerstone of the scientific method. Quantitative research can be pretty fast. The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. Qualitative vs Quantitative Research When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. 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 ... Qualitative and Quantitive Research: What's the Difference? 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. Qualitative vs. Quantitative Research Qualitative research offers the advantage of generating detailed and nuanced data. It allows researchers to explore complex issues and gain a deeper understanding of participants' thoughts, emotions, and behaviors. However, qualitative research can be time-consuming, and data analysis may be subjective. In contrast, quantitative research ... Quantitative and Qualitative Research Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups. Indicators of qualitative research include: What Is Qualitative vs. Quantitative Study? What Is Qualitative vs. Quantitative Study? Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the "how" and "why" of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify ... 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 ... Qualitative vs Quantitative While quantitative research is based on numbers and mathematical calculations (aka quantitative data), qualitative research is based on written or spoken narratives (or qualitative data).Qualitative and quantitative research techniques are used in marketing, sociology, psychology, public health and various other disciplines. SU Library: Qualitative vs. Quantitative Research: Overview In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. Qualitative vs. Quantitative Research: Comparing the Methods and 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 ... Qualitative vs. Quantitative: Key Differences in Research Types Qualitative research uses open-ended methods like interviews, focus groups or content analysis. Nature of data: Quantitative research involves numerical data that you can measure and analyze statistically, whereas qualitative research involves exploring the depth and richness of experiences through nonnumerical, descriptive data. Qualitative vs. Quantitative Research: What's the Difference? Because qualitative and quantitative studies collect different types of data, their data collection methods differ considerably. Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society. Qualitative vs. quantitative research: A simple guide Quantitative research counts and measures numbers to find statistical patterns, while qualitative research is a deep dive into understanding people's thoughts and experiences. They're similar in that they both aim to uncover valuable insights, but they use different tools and approaches to do so. Understand Qualitative vs Quantitative Research Qualitative data provides details and context to better understand individual responses, while quantitative data can supply the cumulative results you need to prove the general ideas or hypotheses of your research. To get the best results from these methods in your surveys, it's important to understand the differences between them. Let's ... Quantitative vs. Qualitative Quantitative Research. Qualitative Research. Tests hypotheses born from theory. Generates understanding from patterns. Generalizes from a sample to the population. Applies ideas across contexts. Focuses on control to establish cause or permit prediction. Focuses on interpreting and understanding a social construction of meaning in a natural setting Quantitative vs. Qualitative? Qualitative vs Quantitative worksheet. Methodology Decision Tree. This diagram should help you to determine whether the research you are looking at is qualitative or quantitative. NOTE: This is a brief guide and might not be correct in every instance << Previous: Annotated Bibliography vs. Literature Review; Exploring Research Design: Qualitative, Quantitative, and Mixed The article discusses blended techniques as well as qualitative and quantitative research methodologies. Both non-experimental methods like surveys and experimental methods are used in qualitative research. Narrative research, phenomenology, case studies, and grounded theory are examples of qualitative research. The influence of cow manure on quantitative and qualitative traits of ABSTRACT. The effects of organic fertilizer "cow manure" applications on quantitative and qualitative traits of hot pepper (Capsicum annuum L.) evaluated under field conditions.Treatments consisted of four levels of cow manure (0, 5, 10, and 15 t ha −1) for both the 2017 and 2018 growing seasons.The results indicated that cow manure applied at 15 t ha −1 resulted in the highest number ... Qualitative and quantitative return-to-sport test battery and second This study examined risk factor of second ACL injury with using quantitative and qualitative measures. A battery of the quantitative tests using such as limb symmetry index is common for RTS tests following ACLR. Hopefully, this study opens the door for a qualitative RTS test evaluation following ACLR. （7） Terminology ※1 ACL Implementation of health-promoting retail initiatives in the Healthier Improving food environments like supermarkets has the potential to affect customers' health positively. Scholars suggest researchers and retailers collaborate closely on implementing and testing such health-promoting interventions, but knowledge of the implementation of such interventions is limited. We explore the implementation of four health-promoting food retail initiatives selected and ... essay homework paper phd project resume review study thesis