is qualitative research better than 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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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

• 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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Criterion Validity: Definition & Examples

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Quantitative vs. Qualitative Research in Psychology

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

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

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

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

At a Glance

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

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

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

Qualitative Research vs. Quantitative Research

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

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

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

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

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

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

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

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

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

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

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

Quantitative methods are about measuring phenomena, not explaining them.

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

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

Basic Assumptions

Quantitative methods assume:

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

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

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

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

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

Correlation and Causation

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

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

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

Pitfalls of Quantitative Research

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

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

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

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

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

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

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

Qualitative Approaches

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

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

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

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

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

Interpretation

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

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

Relationship Between Qualitative and Quantitative Research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Critically Thinking About Qualitative Versus Quantitative Research

What should we do regarding our research questions and methodology.

Posted January 26, 2022 | Reviewed by Davia Sills

• Neither a quantitative nor a qualitative methodology is the right way to approach every scientific question.
• Rather, the nature of the question determines which methodology is best suited to address it.
• Often, researchers benefit from a mixed approach that incorporates both quantitative and qualitative methodologies.

As a researcher who has used a wide variety of methodologies, I understand the importance of acknowledging that we, as researchers, do not pick the methodology; rather, the research question dictates it. So, you can only imagine how annoyed I get when I hear of undergraduates designing their research projects based on preconceived notions, like "quantitative is more straightforward," or "qualitative is easier." Apart from the fact that neither of these assertions is actually the case, these young researchers are blatantly missing one of the foundational steps of good research: If you are interested in researching a particular area, you must get to know the area (i.e., through reading) and then develop a question based on that reading.

The nature of the question will dictate the most appropriate methodological approach.

I’ve debated with researchers in the past who are "exclusively" qualitative or "exclusively" quantitative. Depending on the rationale for their exclusivity, I might question a little deeper, learn something, and move on, or I might debate further. Sometimes, I throw some contentious statements out to see what the responses are like. For example, "Qualitative research, in isolation, is nothing but glorified journalism . " This one might not be new to you. Yes, qualitative is flawed, but so, too, is quantitative.

Let's try this one: "Numbers don’t lie, just the researchers who interpret them." If researchers are going to have a pop at qual for subjectivity, why don’t they recognize the same issues in quant? The numbers in a results section may be objectively correct, but their meaningfulness is only made clear through the interpretation of the human reporting them. This is not a criticism but is an important observation for those who believe in the absolute objectivity of quantitative reporting. The subjectivity associated with this interpretation may miss something crucial in the interpretation of the numbers because, hey, we’re only human.

With that, I love quantitative research, but I’m not unreasonable about it. Let’s say we’ve evaluated a three-arm RCT—the new therapeutic intervention is significantly efficacious, with a large effect, for enhancing "x" in people living with "y." One might conclude that this intervention works and that we must conduct further research on it to further support its efficacy—this is, of course, a fine suggestion, consistent with good research practice and epistemological understanding.

However, blindly recommending the intervention based on the interpretation of numbers alone might be suspect—think of all the variables that could be involved in a 4-, 8-, 12-, or 52-week intervention with human participants. It would be foolish to believe that all variables were considered—so, here is a fantastic example of where a qualitative methodology might be useful. At the end of the intervention, a researcher might decide to interview a random 20 percent of the cohort who participated in the intervention group about their experience and the program’s strengths and weaknesses. The findings from this qualitative element might help further explain the effects, aid the initial interpretation, and bring to life new ideas and concepts that had been missing from the initial interpretation. In this respect, infusing a qualitative approach at the end of quantitative analysis has shown its benefits—a mixed approach to intervention evaluation is very useful.

What about before that? Well, let’s say I want to develop another intervention to enhance "z," but there’s little research on it, and that which has been conducted isn’t of the highest quality; furthermore, we don’t know about people’s experiences with "z" or even other variables associated with it.

To design an intervention around "z" would be ‘jumping the gun’ at best (and a waste of funds). It seems that an exploration of some sort is necessary. This is where qualitative again shines—giving us an opportunity to explore what "z" is from the perspective of a relevant cohort(s).

Of course, we cannot generalize the findings; we cannot draw a definitive conclusion as to what "z" is. But what the findings facilitate is providing a foundation from which to work; for example, we still cannot say that "z" is this, that, or the other, but it appears that it might be associated with "a," "b" and "c." Thus, future research should investigate the nature of "z" as a particular concept, in relation to "a," "b" and "c." Again, a qualitative methodology shows its worth. In the previous examples, a qualitative method was used because the research questions warranted it.

Through considering the potentially controversial statements about qual and quant above, we are pushed into examining the strengths and weaknesses of research methodologies (regardless of our exclusivity with a particular approach). This is useful if we’re going to think critically about finding answers to our research questions. But simply considering these does not let poor research practice off the hook.

For example, credible qualitative researchers acknowledge that generalizability is not the point of their research; however, that doesn’t stop some less-than-credible researchers from presenting their "findings" as generalizable as possible, without actually using the word. Such practices should be frowned upon—so should making a career out of strictly using qualitative methodology in an attempt to find answers core to the human condition. All these researchers are really doing is spending a career exploring, yet never really finding anything (despite arguing to the contrary, albeit avoiding the word "generalize").

The solution to this problem, again, is to truly listen to what your research question is telling you. Eventually, it’s going to recommend a quantitative approach. Likewise, a "numbers person" will be recommended a qualitative approach from time to time—flip around the example above, and there’s a similar criticism. Again, embrace a mixed approach.

What's the point of this argument?

I conduct both research methodologies. Which do I prefer? Simple—whichever one helps me most appropriately answer my research question.

Do I have problems with qualitative methodologies? Absolutely—but I have issues with quantitative methods as well. Having these issues is good—it means that you recognize the limitations of your tools, which increases the chances of you "fixing," "sharpening" or "changing out" your tools when necessary.

So, the next time someone speaks with you about labeling researchers as one type or another, ask them why they think that way, ask them which they think you are, and then reflect on the responses alongside your own views of methodology and epistemology. It might just help you become a better researcher.

Christopher Dwyer, Ph.D., is a lecturer at the Technological University of the Shannon in Athlone, Ireland.

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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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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 .

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American University, EdD in Education Policy and Leadership

Edutopia, “2019 Education Research Highlights”

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

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

Scribbr, “Qualitative vs. Quantitative Research”

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

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

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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.

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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

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• 15 Reasons to Choose Quantitative over Qualitative Research

Researchers often have issues choosing which research method to go with: quantitative or qualitative research methods? Many incorrectly think the two terms can be used interchangeably.

Qualitative research is regarded as exploratory and is used to uncover trends in thoughts and opinions, while quantitative research is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics.

At the end of this article, you will understand why you should consider using quantitative research instead of qualitative method in your research surveys.

Sign up on Formplus Builder to create your preferred online surveys for qualitative and quantitative research. You don’t need any special coding experience! Start now to create research survey questions with Formplus. 

What is Qualitative Research?

Qualitative research is a process of real-life inquiry that aims to understand social phenomena. It focuses on the “why” and “how” rather than the “what” of social phenomena and depends on the direct experiences of human beings as meaning-making agents in their everyday lives.

It is a scientific research method used to gather non-numerical data. Qualitative research focuses on human behavior from a participant’s point of view.

The three major focus areas are individuals, societies and cultures, and language and communication – employed across academic disciplines, qualitative market research, journalism, business, and so on.

Qualitative researchers use varying methods of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory and phenomenology.

The common assumptions are that knowledge is subjective rather than objective and that the researcher learns from the participants in order to understand the meaning of their lives.

Types of Qualitative Research

Just as quantitative, there are varieties of qualitative research methods. We shall look at five types of qualitative research that are widely used in business, education and government organizational models.

• Narrative Research

This method occurs over extended periods of time and garners information as it happens. It laces a sequence of events, usually from just one or two individuals to form a consistent story.

Narrative research can be considered both a research method in itself but also the phenomenon under study.

Businesses use the narrative method to define buyer personas and use them to identify innovations that appeal to a target market.

• Ethnographic Research

This method is one of the most popular and widely recognized methods of qualitative research, as it immerses samples in cultures unfamiliar to them. The researcher is also often immersed as a subject for extended periods of time.

The objective is to understand and describe characteristics of cultures the same way anthropologists observe cultural variations among humans.

“ Ethnographic research allows us to regard and represent the actors as creators and execute their own meanings. The very way in which they tell us about what they do, tells the researcher a great deal about what is meaningful for and in the research. It adds richness and texture to the experience of conducting research .” (Stuart Hannabuss).

The ethnographic method looks at people in their cultural setting; their behavior as well as their words; their interactions with one another and with their social and cultural environment; their language and its symbols; rituals etc. to produce a narrative account of that culture.

Read Also: Ethnographic Research: Types, Methods + [Question Examples]

• Historical Research

This method investigates past events in order to learn present patterns and anticipate future choices. It enables the researcher to explore and explain the meanings, phases and characteristics of a phenomenon or process at a particular point of time in the past.

It is not simply the accumulation of dates and facts or even just a description of past happenings but is a flowing and dynamic explanation or description of past events which include an interpretation of these events in an effort to recapture implications, personalities and ideas that have influenced these events (ibid).

The purpose of historical research is to authenticate and explicate the history of any area of human activities, subjects or events by means of scientific processes (Špiláčková, 2012).

Businesses can use historical data of previous ad campaigns alongside their targeted demographic to split-test new campaigns. This would help determine the more effective campaign.

• Grounded Theory

The grounded theory research method looks at large subject matters and attempts to explain why a course of action progresses the way it did.

Simply put, it seeks to provide an explanation or theory behind the events. Sample sizes are often larger to better establish a theory.

Grounded theory can help inform design decisions by better understanding how a community of users currently use a product or perform tasks. For example, a grounded theory study could involve understanding how software developers use portals to communicate and write code.

Businesses use grounded theory when conducting user or satisfaction surveys that target why consumers use company products or services.

This involves deep understanding through multiple data sources. Case studies can be explanatory, exploratory, or descriptive. 

Unlike grounded theory, the case study method provides an in-depth look at one test subject. The subject can be a person or family, business or organization, or a town or city.

Businesses often use case studies when marketing to new clients to show how their business solutions solve a problem for the subject.

What is quantitative research?

Quantitative research is used to quantify behaviors, opinions, attitudes, and other variables and make generalizations from a larger population. quantitative research uses quantifiable data to articulate facts and reveal patterns in research. This type of research method involves the use of statistical, mathematical tools to derive results.

When trying to quantify a problem, quantitative data will conclude on its purpose and understand how dominant it is by looking for results that can be projected to a larger population.

This data collection method includes various forms of online, paper, mobile, kiosk surveys; online polls; systematic observations; face-to-face interviews, phone interviews and so on.

Researchers who use quantitative research method are typically looking to quantify the degree and accentuate objective measurements through polls, questionnaires, and surveys, or by manipulating an existing statistical data using computational techniques. 

Summarily, the goal in quantitative research is to understand the relationship between an independent and dependent variable in a population.

5 Types of Quantitative Research

There are four main types of quantitative research designs: correlational, descriptive, experimental and quasi-experimental. But there’s another one; survey research.

• Descriptive Research

Descriptive research method is more focused on the ‘what’ of the subject matter rather than the ‘why’.i.e. it aims to describe the current status of a variable or phenomenon.  Descriptive research is pretty much as it sounds – it describes circumstances. It can be used to define respondent characteristics, organize comparisons, measure data trends, validate existing conditions.

Data collection is mostly by observation and the researcher does not begin with a hypothesis but, creates one after the data is collected. Albeit very useful, this method cannot draw conclusions from received data and cannot determine cause and effect. 

• Correlational Research

Correlational research is a non-experimental research method, where the researcher measures two variables, and studies the statistical relationship i.e. the correlation between variables. The researcher ultimately assesses that relationship without influence from any peripheral variable.

Let’s take this example, without classroom teaching, our minds relate to the fact that the ‘louder the jingle of an ice cream truck is, the closer it is to use. We also memorize the jingle that comes from the speakers of the truck. And if there are multiple ice cream trucks in the area with different jingles, we would be able to memorize all of it and relate particular jingles to particular trucks. This is how the correlational method works.

The most prominent feature of correlational research is that the two variables are measured – neither is manipulated.

A correlation has direction and can be either positive or negative. It can also differ in the degree or strength of the relationship.

Read Also: Correlational Research Designs: Types, Examples & Methods

• Experimental Research

Often referred to as ‘true experimentation’, this type of research method uses a scientific method to establish a cause-effect relationship among a group of variables.

It is commonly defined as a type of research where the scientist actively influences something to observe the consequences.

It is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls/randomizes any change in other variables.

Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine and so on.

• Quasi-experimental Research

The prefix quasi means “resembling”. Quasi-experimental research resembles experimental research but is not a true experimental research. It is often referred to as ‘Causal-Comparative’.

In this type of research, the researcher seeks to establish a cause-effect relationship between two variables and manipulates the independent variable.

Although the independent variable is manipulated, participants are not randomly assigned to conditions or orders of conditions (Cook & Campbell, 1979).

Abraham & MacDonald (2011) states:

“ Quasi-experimental research is similar to experimental research in that there is manipulation of an independent variable. It differs from experimental research because either there is no control group, no random selection, no random assignment, and/or no active manipulation .”

Quasi-experimental involves ‘comparison.’ The study of two or more groups is done without focusing on their relationship.

• Survey Research

Survey Research uses interviews, questionnaires, and sampling polls to get a sense of behavior with concentrated precision. Researchers are able to judge behavior and then present the findings in an accurate way.

Survey research can be conducted around one group specifically or used to compare several groups. When conducting survey research, it is imperative that the researcher samples random people. This allows for more accurate findings across a greater number of respondents.

This kind of research can be done in person, over the phone, or through email. They can be self-administered.

Sign up to use Formplus Builder to create your preferred online surveys for qualitative and quantitative research. You don’t need any special coding experience! Start now to create research survey questions with Formplus. 

Why choose Quantitative Research over Qualitative Research?

Quantitative research is more preferred over qualitative research because it is more scientific, objective, fast, focused and acceptable. However, qualitative research is used when the researcher has no idea what to expect. It is used to define the problem or develop and approach to the problem.

• More scientific : A large amount of data is gathered and then analyzed statistically. This almost erases bias, and if more researchers ran the analysis on the data, they would always end up with the same numbers at the end of it.
• Control-sensitive : The researcher has more control over how the data is gathered and is more distant from the experiment. An outside perspective is gained using this method.
• Less biased/objective : The research aims for objectivity i.e. without bias, and is separated from the data. Researcher has clearly defined research questions to which objective answers are sought.
• Focused : The design of the study is determined before it begins and research is used to test a theory and ultimately support or reject it.
• Deals with larger samples : The results are based on larger sample sizes that are representative of the population. The large sample size is used to gain statistically valid results in customer insight.
• Repeatable : The research study can usually be replicated or repeated, given its high reliability.
• Arranged in simple analytical methods : Received data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Generalizable : Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships. Findings can be generalized if selection process is well-designed and sample is representative of a study population.
• Relatable : Quantitative research aims to make predictions, establish facts and test hypotheses that have already been stated. It aims to find evidence which supports or does not support an existing hypothesis. It tests and validates already constructed theories about how and why phenomena occur.
• More structured : Researcher uses tools, such as questionnaires or equipment to collect numerical data.
• Pertinent in later stages of research : Quantitative research is usually recommended in later stages of research because it produces more reliable results.
• Consistent with data : With quantitative research, you may be getting data that is precise, reliable and consistent, quantitative and numerical.
• More acceptable : It may have higher credibility among many influential people (e.g., administrators, politicians, sponsors, donors)
• Fast : Data collection using quantitative methods is relatively quick (e.g., telephone interviews). Also, data analysis is relatively less time consuming (using statistical software).
• Useful for decision making : Data from quantitative research—such as market size, demographics, and user preferences—provides important information for business decisions.

“ There’s no such thing as qualitative data. Everything is either 1 or 0 ” – Fred Kerlinger

When to use Quantitative Research Method

Quantitative research ends with conclusions/recommendations, as it tries to quantify a problem and understand how prevalent it is by looking for results that can be projected to a larger population. It can help you see the big picture.

A researcher may want to determine the link between income and whether or not more people pay taxes. This is a question that asks “how many” and seeks to confirm a hypothesis.

The method will be structured and consistent during data collection, most likely using a questionnaire with closed-ended questions. The data can be used to look for cause and effect relationships and therefore, can be used to make predictions.

The results will provide numerical data that can be analyzed statistically as the researcher looks for a correlation between income and tax payers. Quantitative methodology would best apply to this research problem.

Use quantitative research methods such as A/B testing for validating or choosing a design based on user satisfaction scores, perceived usability measures, and/or task performance. The data received is statistically valid and can be generalized to the entire user population.

Basically, quantitative research is helpful when you get feedback from more than a handful of participants; need to present a more convincing case to an audience; you want to gather feedback from a diverse population of users NOT all located in the same place; you have a limited budget.

When to use Qualitative Research

Qualitative research is explanatory and is used when the researcher has no idea what to expect. It is used to define the problem or develop and approach to the problem.

It is used to delve deeper into issues of interest. Qualitative data adds the details and can also give a human voice to your results.

Use this type of research method if you want to do in-depth interviews, want to analyze issues affecting focus groups, want uninterrupted observation and ethnographic participation.

You can use it to initiate your research by discovering the problems or opportunities people are thinking about. Those ideas can later become hypotheses.

Quotes from open-ended questions in qualitative research can put a human voice to the objective numbers and trends in your results. Many times, it helps to hear your customers describe your organization honestly which helps point out blind spots.

Choose qualitative research if you want to capture the language and imagery customers use to describe and can easily relate with a brand, product, service and so on.

How to Interpret Qualitative Research Data

Qualitative data consists of words, observations, pictures, and symbols. Analyzing received data typically occurs simultaneously with the data collection.

See qualitative research can be analysed and interpreted with the following steps:

• Data familiarity : As a researcher, you should read and understand the data, noting impressions, look for meaning and weed out unnecessary data.
• Identify key questions you want to answer through the analysis. One way to focus the analysis is to examine the data as it relates to a case, an individual, or a particular group.
• Code and index the data by identifying themes and patterns that may consist of ideas, concepts, behaviors, interactions, phrases and so on. Then, assign a code to pieces of data to label the data and make it easier to manage.
• After that, you should identify patterns and make connections. Identify the themes, look for relative importance of responses received and try to find explanations from the data.
• The last thing to do is to interpret the data and explain findings. You can develop a list of key ideas or use models to explain the findings.

How to Interpret Quantitative Research Data

Quantitative research methods result in data that provides quantifiable, objective, and easy to interpret results. Quantitative data can be analyzed in several ways.

The first thing to do for quantitative data is to identify the scales of measurement. There are four levels of measurement: nominal, ordinal, interval, ratio (scale).

Identifying the scale of measurement helps determine how best to organize the data. It can be entered into a spreadsheet and managed in a way that gives meaning to the data.

The next thing to do is to use some of the quantitative data analysis procedures – data tabulation, descriptive data, data disaggregation, moderate and advanced analytics.

Case Study of Quantitative Research

Geramian et al considered the prevalent problem of drug abuse in Iran especially in adolescents and youth, and conducted a study to assess the status of drug abuse among high school students in Isfahan Province, Iran.

The study was conducted through a questionnaire in 2009 in 20 cities. Study population was high school students aged 14–18 years. The required sample size (considering α = 0.05) was calculated as 6489 students, which was increased to 7137 students with consideration of the dropout rate of 10%.

The study identified the degree of drug abuse according to age, gender and cities. There was also an assessment of the type of drugs used, the most common causes of drug abuse for the first time, the most important cause of drug abuse, mean age of abusers and mean age at first abuse, knowledge about short and long-term complications of narcotics and stimulants, common time and locations of drug abuse, and the most common routes of drug abuse according to gender as well as urban and rural areas of Isfahan Province.

Using the results of the research, the knowledge, attitude, and practice of students toward drug abuse were identified.

Case study of Qualitative Research

A good example of qualitative research is Alan Peshkin’s 1986 book God’s Choice: The Total World of a Fundamentalist Christian School published by the University of Chicago Press.

Peshkin examines the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community; and observing for eighteen months – to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

Peshkin’s work represents qualitative research as it is an in-depth study using tools such as observations and unstructured interviews, aimed at securing descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other schools.

Peshkin describes Bethany Baptist Academy as having institutional unity of purpose, a dedicated faculty, an administration that backs teachers in enforcing classroom disciplines, cheerful students, rigorous homework, committed parents, and above all grounded in positive moral values and a character-building environment.

According to Peshkin, the school focuses on providing ‘wholesome’ lives for students, separate from a secular world, however interacting with the same world.

He adds that there is a lack of cultural diversity in the Academy and the counterproductive method of training students in one-dimensional thought, where students are not allowed to question the viewpoints of their teacher’s biblical interpretations; not forgetting the presence of a heavily censored library.

The school also ignores state regulations for schools, such as state assessments, certification and minimum wages for teachers, while enforcing compulsory volunteer tasks for teachers. Peshkin however paints the school in a positive light and holds that public schools have much to learn from such schools.

What is the best Data Collection tool?

Formplus! This is a unique online form tool that lets you collect and manage all the data you need. With Formplus builder, you can create surveys, questionnaires or polls that will help you gather data for your qualitative or quantitative research 

Formplus gives you an easy-to-use form builder with a variety of options including customization to beautify the form in your way.

Signup on Formplus Builder to create your preferred online surveys for qualitative and quantitative research.

Why Formplus is the Best Data Collection Tool for Quantitative & Qualitative Data

Notwithstanding the kind of research you have chosen to do, Formplus offers you amazing features to make your experience simple and easy.

• Collect Data Online

The world is more digital than ever and will become even more digital. Formplus understands this and is giving you a platform to collect store data received from your research, without having to look beyond your shoulders, worrying whether your data is safe or not.

On Formplus, you can create forms for any type of qualitative or quantitative research and you know what? There’s no limit to the amount of online forms you can create.

You can collect all types and sizes of data including typed documents, images, videos and so on.

• Email Invitation

After you have created the online form, you definitely will want to get it to more people so data collection is not restricted.

Use the email invitation feature on Formplus online form to invite people to fill the research form. You can add the emails one after the other, upload a CSV file or populate from an existing database.

• Geolocation

You want to know where responses are coming from? Or concentration of responses from a particular location? Use the geolocation feature, so when responses are submitted, you see the longitude and latitude of the said response.

This will come in handy when you are doing qualitative research for a particular area and want to weed out data coming from other areas.

• Social Media/Website Popup Sharing

It does not end with email invitations, you could share your online forms to Facebook, Twitter or LinkedIn for more responses.

Embed on your website as a popup to make it easy for respondents to click and fill forms right away without leaving your website.

• Export/Data Interpretation

Export received data into another format – PDF or Microsoft Word – make information easy to digest.

Use the exported data to review responses for the research or make comparisons.

On your dashboard, you can view live analytics of responses including abandonment rate, total visits, average time spent and more.

• Storage Integration

Researches always come in with a lot of data but we got you covered. Formplus allows you store unlimited file types and sizes. Added to that are cloud storage integrations to give you options to choose from.

With Formplus, you can decide to use either Google Drive, OneDrive or Dropbox to store and share received data without hassles. All you need to do is connect an existing account you have with either of those three options and you are on your way. You can easily create an account with any of them, if you do not have in easy steps.

• Team & Collaboration

Manage teams for your research to delegate duties to departments or specializations. Add team members and assign roles to them. Restrict their access, also monitor their activities on your account.

Basically, Formplus allows you collaborate with members of the research team to ensure the data is well managed and positive results maintained.

One more thing, even if you give admin access to a team member, you are still in control of your account.

As much as qualitative data adds humanity to data, quantitative data usually comes at the end to use numerical data to make conclusions.

Both qualitative and quantitative research methods have their flaws. However, it is imperative to note that quantitative research method deals with a larger population and quantifiable data and will, therefore, produce a more reliable result than qualitative research.

We provided 15 reasons quantitative research outsmarts qualitative research but you still have doubts? Let’s talk about it. 

• Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.
• Abraham, I. and MacDonald, K. (2011) Encyclopedia Of Nursing Research: Quasi-Experimental Research. Springer Publishing Company. Available here
• Stuart Hannabuss,”Being there: ethnographic research and autobiography”, Library Management, Vol. 21 No. 2.
• Jovita J. Tan (2015), Historical Research: A Qualitative Research Method.

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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 .

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

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• Whether you want a full dissertation written or need help forming a dissertation proposal, we can help you with both.
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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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Thematic analysis is commonly used for qualitative data. Researchers give preference to thematic analysis when analysing audio or video transcripts.

Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement.

A hypothesis is a research question that has to be proved correct or incorrect through hypothesis testing – a scientific approach to test a hypothesis.

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• Qualitative Vs Quantitative Research

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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.

Related resources

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 — A step-wise guide to conduct research

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

What Is Qualitative Research?

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

When to Use Qualitative Research?

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

Benefits of Qualitative Research

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

Qualitative Research Example

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

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

What Is Quantitative Research?

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

When to Use Quantitative Research?

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

Benefits of Quantitative Research

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

Quantitative Research Example

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

Data Collection Methods

1. qualitative data collection methods.

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

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

2. Quantitative Data Collection Methods

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

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

A Step-wise Guide to Conduct Qualitative and Quantitative Research

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

Qualitative Vs. Quantitative Research – A Comparison

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

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

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Qualitative vs Quantitative Research: When to Use Each

User research is crucial for understanding the needs, preferences, and behaviours of your users. By directly engaging with and observing real users, you gain invaluable insights that can inform the design and development of your product or service.

There are two main approaches to conducting user research: qualitative and quantitative.

This article will provide an overview of qualitative vs quantitative research. I’ll define what each method is, walk through example scenarios of when you might use one versus the other, highlight the benefits of each, and offer guidelines on when qualitative or quantitative user research is most appropriate.

With a foundational understanding of these two complementary research approaches, you’ll be equipped to choose the right user research method(s) for gaining the insights you need.

Let’s get started.

Table of Contents

What is user research.

User research is the study of target users and their needs, goals, and behaviours. It provides critical insights that inform the design and development of products, services, and experiences.

The goal of user research is to understand users’ motivations and thought processes so that solutions can be crafted to meaningfully address their pain points and desires. Researchers utilize various qualitative and quantitative techniques to uncover users’ attitudes, perceptions, and needs.

The findings from user research drive design decisions, product strategy, and business objectives. By grounding designs in real user data, teams can create solutions that delight users by meeting their needs. User research provides a profound understanding of the problem space so that products resonate with users’ mental models and workflows.

Qualitative User Research

Qualitative user research is a set of exploratory research techniques focused on developing a deep understanding of why and how people behave, think, feel, and make decisions. 

It typically involves open-ended observations, interviews, and analysis based on small sample sizes. 

The goal is to uncover insights into human motivations, attitudes and needs through immersive and conversational research methods. 

Rather than focusing on quantitative metrics or measurements, qualitative user research aims to understand the nuanced human context surrounding products, services, and experiences.

Key characteristics of qualitative research include:

Asking open-ended questions – 

Qualitative research utilizes flexible, open-ended questions that allow users to provide thoughtful and descriptive responses. Questions focus on the “why” and “how” behind bbehaviours not just surface-level preferences. For example, researchers may ask “Can you walk me through how you accomplished that task?” rather than “Did you find that task easy or difficult?”. Open questions lead to deeper psychological insights.

Small but focused sample sizes – 

Qualitative studies recruit a smaller number of users, but they represent the target audience segment. For example, rather than 500 broadly targeted surveys, qualitative research may study 8-12 users who match the persona. Smaller samples enable more time spent discovering each user’s nuanced perspectives.

Naturalistic observations – 

Qualitative research observes users interacting in real environments, like their homes or workplaces. This naturalistic approach reveals authentic behaviours versus what people say. Researchers can shadow users and see real-world contexts.

Immersive techniques – 

Qualitative research utilizes ethnography-inspired techniques. Researchers embed themselves alongside users to empathize with their worldview. In-depth interviews, diary studies, and field visits all facilitate first-hand experience of the user’s journey – Through open and natural dialogue, qualitative research uncovers emotional and social insights difficult to extract via surveys or analytics. The human-to-human approach highlights feelings, relationships, and unarticulated needs.

Common Qualitative Research Methods

1. one-on-one interviews.

Conducting a one-on-one user interview involves an in-depth, conversational session between the researcher and a single user representative of the target audience. The interviewer guides the discussion using flexible, open-ended questions to elicit deep insights into the user’s perspectives, bebehavioursand needs.

One-on-one interviews shine when:

• Granular insights are needed from individuals based on their distinct circumstances and backgrounds.
• Understanding nuanced personal contexts, thought processes, pain points and emotions is critical.
• Users may be more forthcoming when peaking alone versus groups.
• The order and wording of questions benefit from real-time adaptation to the dialogue flow.
• Non-verbal cues and body language provide additional context to verbal answers.

Effective one-on-one interview tips include:

• Establishing rapport helps the user open up honestly. Avoid an interrogation vibe.
• Adapt questions based on responses, probing for richer details. Don’t just stick to a rigid script.
• Remain neutral and avoid leading questions that influence the user’s answers.
• Listen fully not just for what’s said but also what’s unspoken. Note emotions and inconsistencies.
• Thank the user for generously providing their time and perspectives. They feel valued.

One-on-one engagement allows deep discovery of individual motivations and contexts. It requires planning, active listening, and interpreting both verbal and non-verbal cues.

2. Focus Groups

A focus group brings together 6-12 users from the target audience for a moderated, interactive discussion focused on a product, service, or topic. Participants share perspectives and build on each other’s ideas in a conversational setting.

Focus groups are advantageous when:

• Real-time user interaction and feedback on concepts is desired.
• Sparking new ideas across users with different attitudes and behaviors is the goal.
• Observing how users influence each other reveals social dynamics and norms.
• A wider range of feedback is needed in the time available versus 1-on-1 interviews.

Tips for productive focus groups include:

• Recruit users who offer diverse perspectives but fit the target audience.
• Use a skilled, neutral moderator to facilitate constructive discussion and keep it on track.
• Explain ground rules upfront so all participants engage respectfully.
• Guide the flow from general to specific questions, leaving time for open discussion.
• Change up activities and stimuli (images, prototype demos) to sustain energy.
• Send recordings for further analysis of responses, interactions, and nonverbal behaviors.

3. User Diaries

User diaries involve having target audience members self-document and reflect on their experiences related to a product or service over time in an ongoing journal. Diary studies provide rich, longitudinal insights from the user’s perspective.

Diary studies are advantageous when:

• Capturing detailed, nuanced accounts of user journeys, motivations, pain points, and perceptions in a real-world context is needed.
• Users are geographically dispersed making direct observations or interviews impractical.
• Revealing changes over time rather than one-off interactions is the research goal.
• Users can clearly articulate their experiences through written or multimedia diaries.

Tips for productive diary studies include:

• Provide clear instructions and templates detailing what details to capture in diary entries over the study duration. Offer tools like written journals, audio recorders, or online forms.
• Set reasonable time commitments per day/week and study length based on depth required and user willingness.
• Check-in throughout the process to maintain participation, answer questions, and fix issues.
• Incentivize participation by compensating users for time spent journaling.
• Regularly review entries to identify compelling patterns and follow up for more context.
• Analyze entries to uncover key themes, insights, and opportunities related to the research aims.

Well-designed diary studies generate rich qualitative data by tapping into users’ direct experiences in their own words over time.

4. Ethnographic Studies

This involves immersing in users’ real-world environments to observe behaviors, understand contexts, and uncover unarticulated needs. Researchers embed directly in the user experience.

Ethnographies excel when:

• Deep insight into “unsaid” user behaviors, motivations, and pain points is needed.
• Directly observing users interacting in real environments provides more authenticity than interviews.
• Longer-term immersion reveals ingrained habits, rituals, and relationships.
• Users cannot fully or accurately articulate their own behaviors and motivations.

Tips for effective ethnographies:

• Clearly define the cultural/environmental scope for observations. Get necessary access.
• Utilize fly-on-the-wall observation techniques to avoid disrupting natural behaviors.
• Take comprehensive notes on user activities, interactions, tools, and environmental factors.
• Look for patterns in activities, conversations, rituals, artifacts, and relationships.
• Balance active observation with informal interview discussions to add context.
• Keep the human perspective; focus on empathy not just data gathering.

5. User Testing

User testing involves directly observing representative users interact with a product or prototype to identify usability issues and collect feedback. Participants work through realistic scenarios while researchers analyze successes, pain points, emotions, and verbal commentary.

User testing shines when:

• Feedback is needed on whether designs meet user expectations and needs.
• Identifying issues in workflows, navigation, learnability, and comprehension is important.
• Directly observing user behavior provides more reliable insights than what they self-report.
• Testing with iterations is built into the product development process.

Tips for effective user testing:

• Develop realistic usage scenarios and test scripts tailored to key research questions. Avoid bias.
• Recruit users matching target demographics and familiarity with the product domain.
• Set up comfortable testing spaces and moderation that put users at ease.
• Record sessions to capture insights from body language, tones, facial expressions etc.
• Analyze results for trends and outliers in behaviors, problems, emotions. Focus on learning.
• Iterate on solutions based on insights. Retest with new users to validate improvements.

6. Think-Aloud-Protocol

The think-aloud protocol method asks users to continuously verbalize their thoughts, feelings, and opinions while completing tasks with a product or prototype. Researchers observe and listen as users express in-the-moment reactions.

Think-aloud testing is ideal when:

• Understanding users’ in-the-moment decision making process and emotional responses is invaluable.
• Insights into points of confusion, frustration, delight can rapidly inform design iterations.
• Users can competently complete tasks while articulating their thinking concurrently.
• Limited time is available compared to extensive ethnographies or diary studies.

Effective think-aloud tips include:

• Provide clear instructions to share thoughts continuously throughout the session. Reassure users.
• Use open-ended prompts like “Tell me what you’re thinking” to encourage articulation without leading.
• Avoid interfering with the user’s process so their commentary feels natural.
• Have users complete realistic, task-based scenarios representative of the product experience.
• Capture direct quotes and time stamp compelling reactions to inform development priorities.

Think-aloud testing efficiently provides a window into users’ in-the-moment perceptions and decision making during hands-on product experiences

Applications Of Qualitative Research

Early product development stages:.

Qualitative user research is invaluable in the early ideation and discovery phases of product development when the problem space is still being explored.

Methods like interviews, ethnographies, and diary studies help researchers deeply understand user needs even before product ideas exist. Qualitative data informs initial user personas, journeys, and use cases so product concepts address real user problems.

Early qualitative insights ensure the end solution resonates with user contexts, attitudes, behaviors and motivations. This upfront user-centricity pays dividends across the entire product lifecycle.

Understanding user needs:

Qualitative techniques directly engage with end users to reveal not just what they do, but why they do it. Immersive interviews unveil users’ unstated needs because researchers can ask follow-up questions on the spot.

Observational studies capture nuanced behaviors that users themselves may not consciously realize or find important to mention. The qualitative emphasis on unlocking the “why” behind user actions is crucial for identifying needs that statistics alone miss. The human-centered discoveries spark innovation opportunities.

Problem identification:

The flexible and exploratory nature of qualitative research allows people to openly share the frustrations, anxieties, and pain points they experience.

Their candid words and emotions convey the meaning behind problems far better than numbers alone. For example, ethnographies and diaries may reveal users’ biggest problems stem not from one specific functionality issue but from misaligned workflows overall.

Qualitative techniques dig into the impacts of problems. The human perspectives guide better solutions.

Understanding context of use:

Well-designed qualitative studies meet users in their natural environments and daily lives. This enables researchers to observe how products and services integrate within existing ecosystems, habits, relationships, and workflows.

Key contextual insights are revealed that surveys alone could miss. For example, home interviews may show a smart speaker’s role in family dynamics. Contextual understanding ensures products fit seamlessly into users’ worlds.

Benefits Of Qualitative Research

Gaining deep insights:.

Qualitative techniques like long-form interviews, think-aloud protocol, and diary studies uncover not just surface-level behaviors and preferences, but the deeper meaning, motivations and emotions behind users’ actions.

Asking probing open-ended questions during in-depth conversations reveals nuanced perspectives on needs, thought processes, pain points, and ecosystems.

Immersive ethnographic observation also provides a holistic view of ingrained user habits and contexts. The richness of these qualitative findings informs truly human-centered innovation opportunities in a way quantitative data alone cannot.

Understanding user emotions:

Qualitative research effectively captures the wide range of emotional aspects of the user experience. Through ethnographic observation, researchers directly see moments of delight during usability testing or frustration while completing a task.

Diary studies provide outlets for users to express perceptions in their own words over time.

In interviews, asking follow-up questions on reactions and feelings provides more color than rating scales. This emotional intelligence helps designers move beyond functional requirements to empathetically address felt needs like enjoyment, trust, accomplishment, and belonging.

Exploring new ideas:

The flexible, conversational nature of qualitative research facilitates creative ideation.

Interactive sessions like focus groups or participatory design workshops allow people to organically share, build on, and iterate on ideas together.

Moderators can probe concepts through clarifying, non-leading questions to draw out nuance and have participants riff on each other’s thoughts. This process efficiently fosters new directions and uncovers latent needs that traditional surveys may never have identified.

Uncovering underlying reasons:

Asking “why” is fundamental to qualitative inquiry. Researchers go beyond documenting surface patterns to uncover the deeper motivations, contextual influences, ingrained habits, and thought processes driving user behaviours.

Observations combined with follow-up interviews provide well-rounded explanations for why people act as they do. For example, apparent routines may be based on social norms versus personal preferences. Qualitative findings explain behavior in a way quantitative data alone often cannot.

Facilitating empathy:

Approaches like ethnography facilitate stepping into the user’s shoes to immerse in their worldview.

Two-way dialogue through long-form interviews allows candid exchange as fellow humans, not detached research subjects. Insights derived from conversations and observations in real-world contexts inspire greater empathy among researchers for users’ needs, frustrations, delights, and realities. Teams feel connected to the people they aim to understand and serve.

Quantitative User Research

Quantitative research seeks to quantify user behaviors, preferences, and attitudes through numerical and statistical analysis. It emphasizes objective measurements and large sample sizes to uncover insights that can be generalized to the broader population.

Key characteristics of quantitative research include:

Structured methodology: 

Quantitative studies utilize highly structured data collection methods like surveys, structured user observation, and user metrics tracking. Surveys rely on closed-ended questions with predefined response options. Observation uses systematic checklists to tally predefined behaviors. This standardization allows mathematical analysis across all participants.

Numerical and statistical analysis: 

The numerical data gathered through quantitative research is analyzed using statistics, aggregates, regressions, and predictive modeling to draw conclusions. Researchers can analyze response frequencies, statistical relationships between variables, segmentation analyses, and predictive models based on the quantitative data.

Large representative samples: 

Quantitative research prioritizes large sample sizes that aim to be representative of the target population. For surveys, sufficient sample sizes are determined using power analyses to ensure findings are generalizable. Some common samples can be in the hundreds to thousands. This is in contrast to smaller qualitative samples aimed at diving deep into individual experiences.

Rating scales: 

Surveys and questionnaires rely heavily on numerical rating scales to quantify subjective attributes like satisfaction, ease-of-use, urgency, importance etc. Respondents rank options or choose numbers that correspond to stances. This assigns discrete values for comparison and statistical testing.

Objectivity : 

Quantitative research focuses on uncovering factual, observable and measurable truths about user behaviors, needs or perceptions. There is less emphasis on gathering subjective viewpoints, contexts, and detailed narratives which are hallmarks of qualitative research. The goal is objective, generalizable insights.

Common Quantitative Research Methods

1. online surveys.

Online surveys involve asking a sample of users to respond to a standardized set of questions delivered through web forms or email. Surveys gather self-reported data on attitudes, preferences, needs and behaviors that can be statistically analyzed.

Online surveys are ideal when:

• A large sample size is needed to gain representative insights from a population.
• Standardized, quantitative data on usages, perceptions, features etc. is desired.
• Users have the literacy level to understand and thoughtfully complete surveys.
• Stakeholders want quantitative metrics, benchmarks and models based on user data.

Effective online survey tips:

• Limit survey length and design clear, focused questions to maintain engagement.
• Structure questions and response options to enable statistical analysis for trends and relationships.
• Use rating scales to quantify subjective attributes like satisfaction, urgency, importance etc.
• Write simple, unambiguous statements users can assess consistently. Avoid leading or loaded language.
• Test surveys before deployment to refine questions and ensure technical functionality.
• Analyze results with statistics and visualizations to glean actionable, user-centered insights.

2. Usability Benchmarking

Usability benchmarking involves assessing a product’s ease-of-use against quantified performance standards and metrics. Researchers conduct structured usability tests to gather performance data that is compared to benchmarks.

Usability benchmarking is ideal when:

• Quantitative goals exist for critical usability metrics like task completion rate, errors, time-on-task, perceived ease-of-use.
• Comparing usability data to other products, previous versions, or industry standards is desired.
• There is a focus on improving usability measured through standardized objectives versus qualitative insights.

Effective usability benchmarking tips:

• Identify key usage tasks and scenarios that align to business goals to standardize testing.
• Leverage established usability metrics like System Usability Scale (SUS) to enable benchmarking.
• Conduct structured tests with representative users on targeted tasks.
• Analyze metrics using statistical methods to surface enhancements tied to benchmarks.
• Set incremental usability goals and continue testing post-launch to drive improvements.

3. Analytics

Analytics involves collecting and analyzing usage data from products to uncover patterns, metrics, and insights about real customer behaviors. Sources like web analytics, app metrics, and usage logs are common.

Analytics excel when:

• Objective data on how customers are actually using a product is needed to optimize features and workflows.
• Large volumes of real customer usage data are available for analysis.
• Revealing segments based on behavioral differences can inform personalized experiences.
• Improving key performance indicators and quantifying impact is a priority.

Effective analytics tips:

• Identify key questions and metrics aligned to business goals to focus analysis. Common metrics are conversions, engagement, retention etc.
• Leverage tools like Google Analytics to collect event and behavioral data at scale.
• Analyze trends, run statistical tests, and build models to surface insights from noise.
• Make insights actionable by tying to opportunities like improving at-risk customer retention.
• Continuously analyze data over time and across updates to optimize ongoing enhancements.

Applications of Quantitative Research

Validating hypotheses:.

Quantitative studies provide statistically robust methods to validate assumptions and confirm hypotheses related to user behaviors or preferences.

After initial qualitative research like interviews raise theories about user needs or pain points, quantitative experiments can verify if those hypotheses hold true at a larger scale.

For example, A/B testing can validate if a new checkout flow improves conversion rates as hypothesized based on earlier usability studies. Statistical validation boosts confidence that recommended changes will have the expected impact on business goals.

Generalizing findings:

The large, representative sample sizes and standardized methodologies in quantitative studies allow findings to be generalized to the full target population with known confidence intervals.

Proper sampling methods ensure data reflects the intended audience demographics, attitudes, and behaviours.

If certain usability benchmarks hold true across hundreds of participants, they are assumed to apply to similar users across that segment. This enables product improvements to be made for broad groups based on generalizable data.

Tracking granular changes:

Quantitative data enables even subtle changes over time, iterative tweaks, or segmented differences to be precisely tracked using consistent metrics.

Longitudinal surveys can pinpoint if customer satisfaction trends upward or downward month-to-month based on new features.

Web analytics continuously monitor click-through rates over years to optimize paths. Controlled A/B tests discern the isolated impact of iterative enhancements. The reliability of quantitative metrics ensures changes are spotted quickly.

Quantifying problem severity:

Statistical analysis in quantitative research can accurately define the frequency and severity of user problems.

For example, an eye-tracking study might uncover 60% of users miss a key navigation element. Surveys can determine what percentage of customers are highly frustrated by unclear documentation.

Quantifying the scope and business impact of issues in this way allows product teams to confidently prioritize the problems with greatest value to solve first.

Benefits of Quantitative Research

Quantifying user behaviours:.

Quantitative methods like analytics, surveys, and usability metrics capture concrete, observable data on how users interact with products.

Usage metrics quantify engagement levels, conversion rates, task completion times, feature adoption, and more. The numerical data enables statistical analysis to uncover trends, model outcomes, and optimize products based on revealed behaviours versus subjective hunches. Quantification also facilitates benchmarking and goal-setting.

Validating hypotheses rigorously:

Quantitative experiments like A/B tests and controlled usability studies allow assumptions and theories about user behaviors to be validated with statistical rigour.

Significant results provide confidence that patterns are real and not due to chance alone. Teams can test hypotheses raised in past qualitative research to prevent high-risk decisions based on false premises. Statistical validation lends credibility to recommended changes expected to impact key metrics.

Precisely tracking granular trends:

The consistent, standardized metrics in quantitative studies powerfully track usage trends over time, across releases, and between user segments. For example, longitudinal surveys can monitor how satisfaction ratings shift month-to-month based on new features.

Web analytics uncover how click-through rates trend up or down over years as needs evolve. Controlled tests isolate the impact of each iteration. Quantitative data spots subtle changes.

Informed decision-making:

Quantitative data provides concrete, measurable evidence of user behaviours, needs, and pain points for informed decision-making.

Metrics on usage, conversions, completion rates, satisfaction, and more enable teams to identify and prioritize issues based on representative data versus hunches. Leaders can justify decisions using statistical significance, projected optimization gains, and benchmark comparisons.

Mitigating biases:

The focus on objective, observable metrics can reduce biases that may inadvertently influence qualitative findings.

Proper sampling methods, significance testing, and controlled experiments also minimize distortions from individual perspectives. While no research is assumption-free, quantitative techniques substantially limit bias through rigorous design and large sample sizes.

Comparing Qualitative and Quantitative User Research

Here is a comparison of qualitative and quantitative user research in a table format:

When to Use Each Method

When to use qualitative research:.

• Early in the product development lifecycle during the fuzzy front-end stages. Open-ended qualitative research is critical for discovering user needs, pain points, and behaviors when the problems are unclear. Qualitative data provides the rich contextual insights required to guide initial solution ideation and design before quantifying anything. Methods like in-depth interviews and contextual inquiries reveal pain points that pure quantitative data often overlooks.
• When research questions are ambiguous, expansive, or nuanced at the start. Qualitative methods can flexibly follow where the data leads to uncover unexpected themes. The fluid approach adapts to capture unforeseen insights, especially on subjective topics like emotions and motivations that require deep probing. Qualitative approaches excel at understanding complex “why” and “how” aspects behind behaviors.
• If seeking highly vivid, detailed narratives of user motivations, ecosystems, thought processes, and needs. Qualitative data maintains all the situational nuance and color intact, not condensed statistically. User stories and perspectives come through with empathy and emotion versus sterile numbers. This level of detail informs truly human-centered solutions.
• During discovery of new market opportunities, expanding into new segments, or exploringnew capabilities with many unknowns. Flexible qualitative digging uncovers fresh territories before attempting to quantify anything. Fuzzy front-end exploration is suited to qualitative exploration.

When to use quantitative research:

• To validate assumptions, theories, and qualitative insights at scale using statistical rigor. Quantitative data provides the confidence that patterns seen are significant and not just anecdotal findings. Surveys, controlled experiments, and metrics test hypotheses raised during qualitative discovery. The statistics offer credibility.
• If research questions aim to precisely quantify target audience behaviors, attitudes, and preferences. Quantitative methods objectively measure “what” users do without room for fuzzy interpretation. The numerical data acts as a precise compass for decision-making.
• When clear metrics and benchmarks are required to set optimization goals, compare design solutions, and tightly track progress. Quantitative data delivers concrete KPIs to orient teams and chart enhancement impact.
• To isolate the precise impact of changes over time or between design solutions by tracking standardized metrics. Controlled A/B tests discern what improvements unequivocally moved key metrics versus speculation.

Frequently Asked Questions (FAQs)

1. What is the main difference between qualitative and quantitative user research?

The main difference is that qualitative research aims to uncover the “why” behind user behaviors through subjective, non-numerical data like interviews and observations. Quantitative research focuses on quantifying the “what” through objective, numerical data like metrics and statistics.

2. Can qualitative and quantitative user research be used together?

Absolutely. Many researchers use a mixed methods approach that combines both qualitative and quantitative techniques to get comprehensive insights. Qualitative research can uncover problems to quantify, while quantitative testing can validate qualitative theories.

3. How do I choose between qualitative and quantitative user research?

Choose based on your current product stage, questions, timeline, and resources. Qualitative research is best for exploratory discovery, while quantitative confirms hypotheses. Use qualitative first, then quantitative or a mix of both.

4. What are some common tools for conducting qualitative and quantitative user research?

Qualitative tools include interviews, focus groups, surveys, user testing and more. Quantitative tools include web analytics, App store metrics, usability metrics, controlled experiments and surveys.

5. What are the limitations of qualitative and quantitative user research?

Qualitative findings are not statistically representative. Quantitative data lacks rich behavioral details. Using both offsets the weaknesses.

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The Difference Between Qualitative and Quantitative Research: Explained

Understanding the difference between qualitative and quantitative research is essential for anyone involved in academic or professional research. Qualitative research focuses on exploring and understanding the ‘why’ and ‘how’ of a particular phenomenon, often using methods like interviews, observations, and open-ended surveys. On the other hand, quantitative research emphasizes measurement and quantification, seeking to answer ‘what,’ ‘where,’ and ‘how many’ through statistical analysis and numerical data. Both approaches offer unique insights and play crucial roles in various fields, from social sciences to business and healthcare. By delving into the nuances of qualitative and quantitative research, researchers can choose the most suitable methodology to address their research questions effectively. This distinction forms the backbone of research design and methodology, shaping the way data is collected, analyzed, and interpreted. In this guide, we will explore the key characteristics of qualitative and quantitative research, highlighting their strengths, limitations, and applications in different research contexts.

Qualitative Research

Qualitative Research is a versatile method of inquiry that delves deep into understanding human behavior, experiences, and motivations. It goes beyond surface-level data to explore the intricacies of how individuals or groups perceive and interpret social or human problems. This type of research is invaluable in providing nuanced insights that can shape further quantitative research or inform decision-making processes. Qualitative research stands out for its subjective approach, allowing researchers to capture the complexities of human interactions and societal dynamics. The flexibility inherent in qualitative research methodologies enables researchers to adapt their approaches based on emerging insights or changing research needs.

Common Methods

Common methods employed in qualitative research include: – Interviews – Focus groups – Observations – Content analysis Each offering unique perspectives and data sources.

Key Strengths

One of the key strengths of qualitative research is its ability to uncover hidden patterns, motivations, and behaviors that quantitative methods may overlook. By immersing researchers in the context of the study, qualitative research facilitates a comprehensive understanding of complex phenomena that quantitative data alone cannot provide. Moreover, qualitative research allows for the exploration of diverse viewpoints and experiences, contributing to a more holistic understanding of the subject under investigation.

Insights and Impact

The rich, detailed insights generated through qualitative research play a pivotal role in informing policies, shaping interventions, and advancing knowledge in various fields. In essence, qualitative research serves as a cornerstone in unraveling the intricacies of human behavior and social phenomena, offering a profound understanding that quantitative research alone cannot achieve.

Dynamic Process

Qualitative research involves a dynamic and iterative process. Researchers engage in open-ended discussions, allowing for a deeper exploration of the subject matter. This method encourages flexibility and adaptability, crucial in capturing the evolving nature of human experiences and behaviors. Additionally, qualitative research often involves the researcher immersing themselves in the research environment, gaining firsthand experiences and insights that quantitative data may not capture.

Emphasis on Context

Another key aspect of qualitative research is its emphasis on context. Understanding the context in which behaviors, beliefs, and interactions occur is essential for interpreting the data accurately. By considering the broader social, cultural, and historical context, qualitative researchers can provide rich descriptions and interpretations that offer valuable insights into the complexities of human phenomena.

Interpretive Nature

Furthermore, qualitative research is characterized by its interpretive nature. Researchers analyze data through a subjective lens, acknowledging their own perspectives and biases in the process. This reflexivity enhances the transparency and credibility of the research findings, encouraging a more nuanced understanding of the subject matter. Overall, qualitative research plays a vital role in complementing quantitative approaches, offering depth, context, and rich insights that contribute to a comprehensive understanding of human behavior and social dynamics.

Quantitative Research

Quantitative research involves the collection and analysis of numerical data to understand phenomena. It focuses on quantifying the data and generalizing results from a sample to a population. This type of research is characterized by its structured methodology, statistical analysis, and objectivity. Some key characteristics of quantitative research include:.

Structured Approach : Quantitative research follows a structured and predetermined research design to ensure reliability and validity of results. Researchers use standardized data collection tools and statistical techniques to analyze the data.

Numerical Data : Quantitative research deals with numerical data that can be quantified and analyzed using statistical methods. This data is often collected through surveys, experiments, or existing datasets.

Generalizability : One of the main goals of quantitative research is to generalize the findings from a sample to a larger population. By using random sampling techniques, researchers aim to make inferences about the population based on the sample data.

Objectivity : Quantitative research strives to maintain objectivity and minimize bias in the research process. Researchers use statistical tools to analyze the data objectively and draw conclusions based on evidence.

Advantages of Quantitative Research

Reliability : Quantitative research is known for its reliability due to its structured methodology and statistical analysis. The results are replicable and consistent, providing a solid foundation for making decisions.

Generalizability : The ability to generalize findings to a larger population is a significant advantage of quantitative research. This allows researchers to make broader conclusions and apply the results to real-world scenarios.

Objectivity : Quantitative research promotes objectivity by relying on numerical data and statistical analysis. This helps in reducing bias and subjectivity in the research process.

Data-driven Decisions : The numerical data collected in quantitative research enables data-driven decision-making. Organizations and policymakers can use the research findings to inform strategies and policies based on empirical evidence.

Statistical Analysis : Quantitative research allows for sophisticated statistical analysis, providing deeper insights into relationships between variables. Researchers can identify patterns, trends, and correlations in the data to draw meaningful conclusions.

Quantitative research offers a systematic and objective approach to studying phenomena through numerical data analysis. Its emphasis on reliability, generalizability, and objectivity makes it a valuable research method in various fields.

When it comes to research methodologies, one of the key differentiators is the distinction between qualitative and quantitative research. Qualitative research focuses on understanding human behavior, emotions, and experiences through methods such as interviews, observations, and case studies. On the other hand, quantitative research involves the collection and analysis of numerical data to identify patterns and relationships.

In terms of data collection, qualitative research relies on non-numerical data, such as words, images, and observations, to gain insights into the research topic. This approach allows researchers to explore complex phenomena in depth and understand the context in which they occur. In contrast, quantitative research involves the collection of numerical data through surveys, experiments, or statistical analysis. This data is then analyzed using statistical methods to draw conclusions and make predictions.

The main differences between qualitative and quantitative research lie in their approaches to data collection and analysis. While qualitative research focuses on exploring and understanding phenomena in their natural settings, quantitative research aims to measure and quantify relationships between variables. Both methodologies have their strengths and limitations, and the choice between them depends on the research questions, objectives, and context of the study.

Qualitative research often involves smaller sample sizes compared to quantitative research. This is because qualitative studies delve deep into individual experiences and behaviors, requiring intensive data collection and analysis. On the other hand, quantitative research typically involves larger sample sizes to ensure statistical significance and generalizability of findings.

Another key aspect to consider is the nature of data analysis. Qualitative research employs techniques such as thematic analysis, content analysis, and grounded theory to interpret textual or visual data. Researchers immerse themselves in the data to identify patterns and themes that emerge. In contrast, quantitative research utilizes statistical tools like regression analysis, correlation, and hypothesis testing to analyze numerical data and test hypotheses.

Furthermore, qualitative research is often exploratory and iterative, allowing researchers to adapt their methods and theories based on emerging findings. This flexibility enables a deeper understanding of complex phenomena and can lead to new insights and theories. In comparison, quantitative research follows a more structured and rigid approach, with predefined hypotheses and data collection methods.

While qualitative and quantitative research differ in their fundamental approaches, both play crucial roles in advancing knowledge and understanding in various fields. Researchers often combine these methodologies to gain a comprehensive understanding of a research problem, leveraging the strengths of each approach to produce robust and insightful results.

Applications

When it comes to research methodologies, both qualitative and quantitative research play crucial roles in gathering data and insights. Understanding when to use each approach is essential for conducting effective research.

When to Use Qualitative Research:

Qualitative research is typically used to explore and understand phenomena in-depth. It is best suited for situations where you want to uncover underlying motivations, attitudes, or behaviors. Qualitative research methods include interviews, focus groups, observations, and case studies. Use qualitative research when: – You want to gain a deeper understanding of a particular topic. – You need to explore complex issues or phenomena. – You want to generate hypotheses for further research.

When to Use Quantitative Research:

Quantitative research involves the collection and analysis of numerical data. It is used to quantify relationships, test hypotheses, and generalize results to a larger population. Quantitative research methods include surveys, experiments, and statistical analysis. Use quantitative research when: – You need to measure the prevalence of a particular phenomenon. – You want to test specific hypotheses and relationships. – You aim to make statistically significant conclusions.

The choice between qualitative and quantitative research depends on the research objectives, the nature of the research questions, and the type of data needed to answer those questions. Both methodologies have their strengths and limitations, and often a combination of both approaches can provide a more comprehensive understanding of a research problem.

Expanding on the applications of qualitative research, it is important to note that this method is highly effective in exploratory studies. Qualitative research allows researchers to delve deep into the subject matter, uncovering nuances that quantitative methods may overlook. It is particularly useful in fields such as sociology, anthropology, and psychology where understanding human behavior and motivations is paramount.

On the other hand, quantitative research excels in providing numerical data that can be analyzed statistically. This method is valuable in studies that require precise measurements and the ability to draw generalizable conclusions. Fields like economics, marketing, and public health often rely on quantitative research to make data-driven decisions.

Moreover, a mixed-methods approach, combining both qualitative and quantitative techniques, can offer a more holistic view of a research problem. By triangulating data from different sources, researchers can validate findings and enhance the overall credibility of their research outcomes.

In practice, researchers should carefully consider the research questions, available resources, and desired outcomes when selecting the appropriate research methodology. By aligning the chosen method with the research objectives, researchers can ensure the validity and reliability of their study results.

Understanding the difference between qualitative and quantitative research is essential for conducting thorough and effective studies. While qualitative research focuses on exploring ideas and experiences in-depth, quantitative research emphasizes numerical data and statistical analysis. Both approaches offer valuable insights and play distinct roles in the research process. To excel in academia and research, mastering the art of writing scientific abstracts is equally crucial. Crafting a concise and engaging abstract can significantly impact the reception of your work. For detailed guidance on writing effective scientific abstracts, check out this helpful website Avidnote . Mastering this skill will not only enhance your academic writing but also improve the visibility and impact of your research.

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Quantitative Data vs Qualitative Data: What's the Difference?

Quantitative data is numerical, countable, or measurable, while qualitative data is descriptive and interpretation-based. Quantitative data answers questions like how many, how much, or how often, whereas qualitative data helps understand why, how, or what happened. Both types of data are valuable and can complement each other to provide a broader view of a subject.

When should you use quantitative data?

Quantitative data is best used to confirm or test theories or hypotheses. It is objective and numerical, answering questions like "what" and "how often." This type of data helps identify specific problems by measuring the "what" and the "how." It can be quickly analyzed and understood, providing hard data, but may oversimplify complex issues.

Key Benefits of Using Quantitative Data

• Quantitative data provides objective, numerical information that can be easily measured and analyzed, making it ideal for confirming theories or hypotheses.
• This type of data can be analyzed using statistical techniques, allowing for precise calculations and the identification of patterns and trends.
• Quantitative research often involves larger sample sizes, which can enhance the reliability and generalizability of the findings.
• Quantitative data can be quickly collected and analyzed, providing timely insights that can inform decision-making processes.

When should you use qualitative data?

Qualitative data is best used to understand concepts, thoughts, or experiences. It is descriptive and involves observations, feelings, and opinions that are difficult to measure objectively. This type of data answers questions like "why" and "how," focusing on subjective experiences to uncover motivations and reasons behind behaviors.

Key Benefits of Using Qualitative Data

• Qualitative data provides rich, detailed information that can help researchers understand the underlying reasons and motivations behind behaviors and attitudes.
• This type of data is ideal for exploratory research, helping to uncover deeper insights and generate new ideas or hypotheses.
• Qualitative data is often used to explore human behavior, attitudes, opinions, and experiences, offering a more nuanced understanding of complex issues.
• Qualitative research methods, such as interviews and observations, allow for flexibility and adaptability in data collection, enabling researchers to explore unexpected findings.

How can quantitative and qualitative data be used together?

Quantitative and qualitative data can be used together to provide a comprehensive understanding of a subject. Quantitative data can identify patterns and measure the extent of an issue, while qualitative data can explain the reasons behind these patterns. Together, they offer a balanced view, combining numerical precision with in-depth insights.

Benefits of Combining Quantitative and Qualitative Data

• Combining both types of data allows for a more thorough analysis, capturing both the breadth and depth of a subject.
• Using both data types can validate findings, as quantitative data provides statistical support while qualitative data offers contextual understanding.
• This approach provides a balanced perspective, integrating numerical data with human experiences and insights.
• A comprehensive understanding of an issue can lead to more informed and effective decision-making, addressing both the "what" and the "why."

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Secoda simplifies data management by integrating multiple tools into one platform, powered by AI. It connects to all data sources, including databases, warehouses, pipelines, models, and visualization tools, allowing users to find and understand information quickly. Features like governance data, bulk updates, PII data tagging, and tech debt management streamline data processes, making it easier for teams to manage and utilize their data efficiently.

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Heavy rainfall event in Nova Friburgo (Brazil): numerical sensitivity analysis using different parameterization combinations in the WRF model

• Original Paper
• Published: 26 May 2024

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• Carolina Veiga   ORCID: orcid.org/0000-0002-6266-4836 1 , 3   na1 ,
• Maria Gertrudes Alvarez Justi da Silva 1   na1 &
• Fabricio Polifke da Silva 2   na1  

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Forecasting rainfall is essential for warning of issues and mitigating natural disasters. For this purpose, employing numerical weather models, even with their uncertainties, can generate reliable forecasts and guide decision-makers. The accuracy of a numerical model can be verified using statistical tools, and it is an essential procedure that needs to be made operationally, aiming to increase the forecasts' reliability. Numerical precipitation forecasts for the mountainous region of Rio de Janeiro, Brazil, were performed using the Weather Research & Forecasting model, configured with three spatial resolution grids of 9, 3, and 1 km, and combining different parameterizations for five physical processes: cloud microphysics, cumulus, planetary boundary layer, surface layer, and land surface. The period of interest was January 11th–12th, 2011, when significant rainfall accumulations originated the fatal natural hazards in Brazil. Analyses of the spatial distribution of rainfall and its temporal evolution were performed to evaluate the predictions from the quantitative and qualitative approaches. The results showed that the Kessler (cloud microphysics), MYNN3 (planetary boundary layer), Grell-Freitas, Betts-Miller-Janjic (cumulus) parameterizations, and the two highest resolution grids (at times, one was better than the other) had predicted the highest rainfall accumulations. From the initial results, this work reinforces the importance of forecast verification, especially considering different physical parameterizations and spatial resolutions since they can strongly influence the results. Also, it corroborates the importance of local numerical forecasts studies aiming to identify the best numerical configurations to forecast heavy rainfall events to alert decision-makers to the possibility of a natural hazard.

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Afshar MA, Azadi M (2020) Uncertainty reduction in quantitative precipitation prediction by tuning of Kain-Fritch scheme input parameters in the WRF model using the simulated annealing optimization method. Meteorol Appl 27:2022. https://doi.org/10.1002/met.1919 . Accessed on 05 May

Article   Google Scholar  

ANA-Agência Nacional de Águas (2021): Hidroweb, https://www.snirh.gov.br/hidroweb/apresentacao . Accessed on 05 May 2022

Battisti R, Bender FD, Sentelhas PC (2019) Assessment of different gridded weather data for soybean yield simulations in Brazil. Theor Appl Climatol 135:237–247. https://doi.org/10.1007/s00704-018-2383-y

BBC News Brasil (2021) Chuvas na Bahia: os fenômenos extremos que causam a tragédia no Estado. BBC News Brasil. https://www.bbc.com/portuguese/brasil-59804297 . Accessed on 05 May 2022

Bender FD, Ynoue RY (2014) Application of a spatial verification method to GFS precipitation forecasts. Braz J Geophys. https://doi.org/10.22564/rbgf.v32i3.519

Busch A, Amorim S (2011) A tragédia da Região Serrana do Rio de Janeiro em 2011: procurando respostas, Tech. Re 2, Escola Nacional de Administração Pública (ENAP). Accessed on 5 May 2022.

Cavalcante MRG, Luz Barcellos PC, Cataldi M (2020) Flash flood in the mountainous region of Rio de Janeiro state (Brazil) in 2011: part I—calibration watershed through hydrological SMAP model. Natural Hazards 102:1117–1134. https://doi.org/10.1007/s11069-020-03948-3

Chen L, Li G, Zhang F, Wang C (2020) Simulation uncertainty of near-surface wind caused by boundary layer parameterization over the complex terrain. Front Energy Res 30:2022. https://doi.org/10.3389/fenrg.2020.554544 . Accessed on 05 May

Chinta S, Sai JY, Balaji C (2021) Assessment of WRF model parameter sensitivity for high-intensity precipitation events during the indian summer monsoon. Earth and Science 8:2022. https://doi.org/10.1029/2020EA001471 . Accessed on 05 May

CPTEC - Centro de Previsão de Tempo e Estudos Climáticos (2011) Climanálise Boletim Janeiro 2011. 26(1). http://climanalise.cptec.inpe.br/~rclimanl/boletim/index0111.shtml . Accessed on 22 Feb 2022.

Dudhia J (1996) A multi-layer soil temperature model for MM5. The Sixth PSU/NCAR Mesoscale Model Users Workshop

G1 ES (2022) Chuva forte deixa mais de mil pessoas fora de casa no ES. G1 Espírito Santo. https://g1.globo.com/es/espirito-santo/noticia/2022/02/20/chuva-forte-deixa-mais-de-mil-pessoas-fora-de-casa-no-es.ghtml . Accessed on 5 May 2022

Fonseca R, Martín-Torres J, Anderson K (2018) Wind forecasts for rocket and balloon launches at the Esrange space center using the WRF model. Weather Forecast 33:813–833. https://doi.org/10.1175/waf-d-18-0031.1 . Accessed on 20 Feb 2022

Grell GA (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Weather Rev 121:764–787

Grell GA, Devenyi DA (2002) Generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29:1693

Grell GA, Freitas SR (2014) A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos Chem Phys 14:5233–5250

Article   CAS   Google Scholar  

Hong SY, Lim JO (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151

Google Scholar  

Hong SY, Pan HL (1996) Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon Weather Rev 124:2322–2339

INEA - Instituto Estadual do Ambiente do Rio de Janeiro (2021) Sistema de alerta de cheias. http://alertadecheias.inea.rj.gov.br/ . Accessed 01 Dec 2021

Jacondino WD, Nascimento ALS, Calvetti L, Fisch G, Beneti AAC, Da Paz SR (2021) Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model. Energy 230:2022. https://doi.org/10.1016/j.energy.2021.120841 . Accessed on 25 Mar

Janjic ZI (1994) The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Monthly Weather Review, American Meteorological Society, Boston MA, USA, v. 122(5), p. 927–945. https://journals.ametsoc.org/view/journals/mwre/122/5/1520-0493_1994_122_0927_tsmecm_2_0_co_2.xml

Javanshiri Z, Fathi M, Mohammadi SA (2021) Comparison of BMA and EMOS statistical methods for probabilistic quantitative precipitation forecasting. Meteorol Appl 28:2022. https://doi.org/10.1002/met.1974 . Accessed on 20 Apr

Jiménez PA, Dudhia J, González-Rouco JF, Navarro J, Montávez JP, García-Bustamante E (2012) A revised scheme for the WRF surface layer formation. Mon Weather Rev 140:898–918

Jin J, Che Y, Zheng J, Xiao F (2019) Uncertainty quantification of a coupled model for wind prediction at a wind farm in Japan. Energies 12:2022. https://doi.org/10.3390/en12081505 . Accessed on 25 Feb

Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol Climatol 43:170–181

Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. Meteorol Monographs 32.

Kruse T (2022) Trágico verão brasileiro já fez maior número de vítimas em 10 anos. https://veja.abril.com.br/coluna/maquiavel/tragico-verao-brasileiro-ja-fez-maior-numero-de-vitimas-em-10-anos/ . Accessed on 05 May 2022

Luz Barcellos PC, Cataldi M (2020) Flash Flood and Extreme Rainfall Forecast through One-Way Coupling of WRF-SMAP Models: Natural Hazards in Rio de Janeiro State. Atmosphere 11. https://www.mdpi.com/2073-4433/11/8/834/htm . Accessed on 02 May 2022

Machado L (2022) Cidades mais atingidas por deslizamentos tiveram 'boom' habitacional em áreas de risco. BBC News Brasil São Paulo. https://www.bbc.com/portuguese/brasil-60209183 . Accessed on 05 May 2022

Marques A, Machado L, Kapiche A (2022) Temporal em Petrópolis deixa 104 mortos; MP registrou 35 desaparecidos. G1 Petrópolis. https://g1.globo.com/rj/regiao-serrana/noticia/2022/02/16/petropolis-estima-80-casas-atingidas-por-deslizamento-no-morro-da-oficina.ghtml . Accessed on 28 Mar 2022

Mello JB (2013) Chuva deixa mais de 10 mortos em Petrópolis, na Região Serrana do RJ. G1 Rio. https://g1.globo.com/rio-de-janeiro/noticia/2013/03/chuva-deixa-mais-de-10-mortos-em-petropolis.html . Accessed on 28 Mar 2022

Mizutori M, Guha-Sapir D (2020) Human cost of disasters 2000–2019. Tech. rep., United Nations Office for Disaster Risk Reduction

Nakanishi M, Niino H (2006) An improved Mellor-Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog. Bound Layer Meteor 119:397–407

Nakanishi M, Niino H (2009) Development of an improved turbulence closure model for the atmospheric boundary layer. J Meteor Soc Japan 87:895–912

National Centers for Environmental Prediction, National Weather Service, NOAA (2020) Global forecast system. https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs

NOAA (2001) Changes to the NCEP meso Eta analysis and forecast system: Increase in resolution, new cloud microphysics, modified precipitation assimilation, modified 3DVAR analysis

Niu G-Y, Yang Z-L, Mitchell KE, Chen F, Ek MB, Barlage M, Kumar A, Manning K, Niyogi D, Rosero E, Tewari M, Xia Y (2011) The community Noah land surface model with multiparameterization options (Noah-MP): 1. model description and evaluation with local-scale measurements. J Geophys Res 116:D12109

Njuki SM, Mannaerts CM, Su Z (2022) Influence of planetary boundary layer (PBL) parameterizations in the Weather Research and Forecasting (WRF) model on the retrieval of surface meteorological variables over the Kenyan highlands. Atmosphere 13:2022. https://doi.org/10.3390/atmos13020169 . Accessed on 25 Mar

Onwukwe C, Jackson PL, Déry SJ (2022) Intercomparison of atmospheric forcing datasets and two PBL schemes for precipitation modelling over a coastal valley in northern British Columbia, Canada. Meteorol Appl. https://doi.org/10.1002/met.2043

Pahlavan R, Moradi M, Tajbakhsh S, Azadi M, Rahnama M (2021) Fog probabilistic forecasting using an ensemble prediction system at six airports in Iran for 10 fog events. Meteorol Appl 28:2022. https://doi.org/10.1002/met.2033 . Accessed on 1 May

Palmer T, Hagedorn R (2006) Predictability of weather and climate. Cambridge University Press, Cambridge

Book   Google Scholar  

Paulson CA (1970) The mathematical representation of wind speed and temperature profiles in the unstable atmospheric surface layer. J Appl Meteor 9:857–861

Pinheiro H, Andrade K, Moura C (2011) A maior catástrofe climática do Brasil sob a visão operacional do CPTEC/INPE. International Symposium on Climatology

PRODERJ – Centro de Tecnologia de Informação e Comunicação do Estado do Rio de Janeiro (2019) Mapa das Regiões de Governo e Municípios do Estado do Rio de Janeiro. http://arquivos.proderj.rj.gov.br/sefaz_ceperj_imagens/Arquivos_Ceperj/ceep/informacoes-do-territorio/cartografia-fluminense/Mapa%20das%20Regi%C3%B5es%20de%20Governo%20e%20Munic%C3%ADpios%20do%20Estado%20do%20Rio%20de%20Janeiro%20-%202019%20-%20CEPERJ.pdf . Accessed on 22 Feb 2022

Reboita MS, Gan MA, Rocha RP, Ambrizzi T (2010) Regimes de precipitação na América do Sul: uma revisão bibliográfica. Revista Brasileira De Meteorologia 25(2):185–204. https://doi.org/10.1590/s0102-77862010000200004 . Accessed 22 Feb

Remesan R, Bellerby T, Holman I, Frostick L (2015) WRF model sensitivity to choice of parameterization: a study of the ’York flood “1999.” Theor Appl Climatol 122:229–247

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Liu Z, Berner J, Wang W, Powers JG, Duda MG, Barker DM, Huang X-Y (2019) A description of the advanced research WRF model version 4. NCAR Tech. Note NCAR/TN-556+STR

Sousa NV, Silva AFG, Fisch GF, Leal JBV Jr, Silva EM (2019) Evaluation of the planetary boundary layer parameterizations of wrf model in north northeast coast at Brazil. Rev Bras Meteorol 34:2022. https://doi.org/10.1590/0102-7786334019 . Accessed 25 Mar

Spiridonov V, Vaez J, Telenta B, Jakimovski B (2020) Prediction of extreme convective rainfall intensities using a free-running 3-D sub-km-scale cloud model initialized from WRF km-scale NWP forecasts. J Atmos Solar-Terr Phys 109:2022. https://doi.org/10.1016/j.jastp.2020.105401 . Accessed on 25 Mar

Suárez-Molina D, Fernández-González F, Montero G, Oliver A, González CS (2021) Sensitivity analysis of the WRF model: assessment of performance in high resolution simulations in complex terrain in the Canary Islands. Atmos Res 247:2022. https://doi.org/10.1016/j.atmosres.2020.105157 . Accessed on 20 Feb

Taraphdar S, Pauluis OM, Xue L, Liu C, Rasmussen R, Ajayamohan RS, Tessendorf S, Jing X, Chen S, Grabowski WW (2021) WRF gray zone simulations of precipitation over the Middle-East and the UAE: impacts of physical parameterizations and resolution. J Geophys Res Atmos 126:2022. https://doi.org/10.1029/2021JD034648 . Accessed on 25 Mar

Tewari M, Chen F, Wang W, Dudhia J, LeMone MA, Mitchell K, Ek M, Gayno G, Wegiel J, Cuenca RH (2004): Implementation and verification of the unified NOAH land surface model in the WRF model. In: 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, pp. 11–15

Thongthammachart T, Araki S, Shimadera H, Eto S, Matsuo T, Kondo A (2021) An integrated model combining random forecast and WRF/CMAQ model for high accuracy spatiotemporal PM.sub.2.5 predictions in the Kansai region of Japan. Atmos Environ. https://doi.org/10.1016/j.atmosenv.2021.118620

Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large–scale models. Mon Wea Rev 117:1779–1800. https://doi.org/10.1175/1520-0493(1989)117%3c1779:ACMFSF%3e2.0.CO;2

Treinish L, Praino A, Cipriani J, Mello U, Real LV, Sesini P, Mantripragada K, Mittal R, Saxena V, George T (2013) Enabling a high-resolution, coupled hydro-meteorological system for operational forecasting of severe weather and flooding events in Rio de Janeiro. Conference on Transition of Research to Operations, 3. https://ams.confex.com/ams/93Annual/webprogram/Paper221567.html . Accessed on 05 May 2022

Wang Y, Feng J, Luo M, Wang J, Qiu Y (2020) Uncertainties in simulating central Asia: sensitivity to physical parameterizations using weather research and forecasting model. Int J Climatol 40:2022. https://doi.org/10.1002/joc.6567 . Accessed on 25 Mar

Wang C, Qian Y, Duan Q, Huang M, Yang Z, Berg LK, Gustafson WI, Feng Z, Liu J, Quan J (2021) Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon. Atmos Res 262:2022. https://doi.org/10.1016/j.atmosres.2021.105761 . Accessed 25 Mar

Wang W, Bruyère C, Duda M, Dudhia J, Gill D, Kavulich M, Keene K, Chen M, Lin H-C, Michalakes J, Rizvi S, Zhang X, Berner J, Ha S, Fossell K (2018) User's guide for the advanced research WRF (ARW) modeling system version 4.0

Warner TT (2010) Numerical weather and climate prediction. Cambridge University Press, Cambridge

World Meteorological Organization (2021) WMO Atlas of mortality and economic losses from weather, climate, and water extremes (1970–2019). Geneva, Switzerland. https://unfccc.int/documents/306865?gad_source=1&gclid=CjwKCAjwi_exBhA8EiwA_kU1Mh-%20Ue6RrVfcNqv8koi52oCTE3a0j6km_0Cqk5FEGPccpLOCPxE_XlRoCEIIQAvD_BwE

Webster P (2013) Improve weather forecasts for the developing world. Nature 493:17–19

Wehbe Y, Temimi M, Weston M, Chaouch N, Branch O, Schwitalla T (2019) Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data. Nat Hazard Earth Syst Sci 19(6):1129–1149

Weston M, Temimi M, Fonseca RM, Nelli NR, Francis D, Piketh S (2021) A rule-based method for diagnosing radiation fog in an arid region from NWP forecasts. J Hydrol 597:2022. https://doi.org/10.1016/j.jhydrol.2021.126189 . Accessed on 20 Feb

Wilks DS (2011) Statistical methods in the atmospheric science. Academic Press, Cambridge

Yang Q, Yu Z, Wei J, Yang C, Gu H, Xiao M, Shang S, Dong N, Gao L, Arnault J, Laux P, Kunstmann H (2021) Performance of the WRF model in simulating intense precipitation events over the Hanjiang River Basin, China–a multi-physics ensemble approach. J Geophys Res Atmos 248:2022. https://doi.org/10.1016/j.atmosres.2020.105206 . Accessed on 25 Mar

Yang Q, Zhang S, Dai Q, Zhuang H (2022) WRF rainfall modeling post-processing by adaptative parameterization of raindrop size distribution: a case study on the United Kindom. Atmosphere 13:2022. https://doi.org/10.3390/atmos13010036 . Accessed on 25 Mar

Yang Z-L, Niu G-Y, Mitchell KE, Chen F, Ek MB, Barlage M, Longuevergne L, Manning K, Niyogi D, Tewari M, Xia Y (2011) The community Noah land surface model with multiparameterization options (Noah-MP): 2. evaluation over global river basins. J Geophys Res 116

Yoon JW, Lim S, Park SK (2021) Combinational optimization of the WRF physical parameterization schemes to improve numerical sea breeze prediction using micro-genetic algorithm. Appl Sci 11:2022. https://doi.org/10.3390/app112311221 . Accessed on 25 Mar

Zhang C, Wang Y, Hamilton K (2011) Improved representation of boundary layer clouds over the southeast pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon Wea Rev 139:3489–3513. https://doi.org/10.1175/MWR-D-10-05091.1

Zhang Y, Yan D, Wen X, Li D, Zheng Z, Zhu X, Wang B, Wang C, Wang L (2020) Comparative analysis of the meteorological elements simulated by different land surface process schemes in the WRF model in the Yellow River source region. Theor Appl Climatol 139:145–162. https://doi.org/10.1007/s00704-019-02955-0 . Accessed on 20 Feb

Zittis G, Bruggeman A, Camera C, Hadjinicolaou P, Lelieveld J (2017) The added value of convection permitting simulations of extreme precipitation events over the eastern Mediterranean. Atmos Res 191:2022. https://doi.org/10.1016/j.atmosres.2017.03.002 . Accessed on 25 Mar

Tao W-K, Simpson J, McCumber M (1989) An ice–water saturation adjustment. Mon Wea Rev 117:231–235. https://doi.org/10.1175/1520-0493(1989)117<0231:AIWSA>2.0.CO;2

Tao W-K, Wu D, Lang S, Chern J-D, Peters-Lidard C, Fridlind A, Matsui T (2016) High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations. J Geophys Res Atmos 121:1278–1305. https://doi.org/10.1002/2015JD023986

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Carolina Veiga Ferreira de Souza, Maria Gertrudes Alvarez Justi da Silva, Fabricio Polifke da Silva have contributed equally to this work.

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Carolina Veiga & Maria Gertrudes Alvarez Justi da Silva

Department of Meteorology, Institute of Geosciences, Universidade Federal do Rio de Janeiro-UFRJ, CCMN‑Cidade Universitária-Ilha do Fundão, Avenida Athos da Silveira Ramos, 274, Rio de Janeiro, RJ, 21941‑916, Brazil

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Veiga, C., da Silva, M.G.A.J. & da Silva, F.P. Heavy rainfall event in Nova Friburgo (Brazil): numerical sensitivity analysis using different parameterization combinations in the WRF model. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06638-6

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Quantitative and Qualitative Approaches to Generalization and Replication–A Representationalist View

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se , but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

Introduction

Qualitative and quantitative research strategies have long been treated as opposing paradigms. In recent years, there have been attempts to integrate both strategies. These “mixed methods” approaches treat qualitative and quantitative methodologies as complementary, rather than opposing, strategies (Creswell, 2015 ). However, whilst acknowledging that both strategies have their benefits, this “integration” remains purely pragmatic. Hence, mixed methods methodology does not provide a conceptual unification of the two approaches.

Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007 ). These different standards affect the way researchers handle contradictory empirical findings. For example, many empirical findings in psychology have failed to replicate in recent years (Klein et al., 2014 ; Open Science, Collaboration, 2015 ). This “replication crisis” has been discussed on statistical, theoretical and social grounds and continues to have a wide impact on quantitative research practices like, for example, open science initiatives, pre-registered studies and a re-evaluation of statistical significance testing (Everett and Earp, 2015 ; Maxwell et al., 2015 ; Shrout and Rodgers, 2018 ; Trafimow, 2018 ; Wiggins and Chrisopherson, 2019 ).

However, qualitative research seems to be hardly affected by this discussion. In this paper, we argue that the latter is a direct consequence of how the concept of generalizability is conceived in the two approaches. Whereas most of quantitative psychology is committed to a top-down strategy of generalization based on the idea of random sampling from an abstract population, qualitative studies usually rely on a bottom-up strategy of generalization that is grounded in the successive exploration of the field by means of theoretically sampled cases.

Here, we show that a common methodological framework for qualitative and quantitative research methodologies is possible. We accomplish this by introducing a formal description of quantitative and qualitative models from a representationalist perspective: both approaches can be reconstructed as special kinds of representations for empirical relational structures. We then use this framework to analyze the generalization strategies used in the two approaches. These turn out to be logically independent of the type of model. This has wide implications for psychological research. First, a top-down generalization strategy is compatible with a qualitative modeling approach. This implies that mainstream psychology may benefit from qualitative methods when a numerical representation turns out to be difficult or impossible, without the need to commit to a “qualitative” philosophy of science. Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory.

The Quantitative Strategy–Numbers and Functions

Quantitative science is about finding valid mathematical representations for empirical phenomena. In most cases, these mathematical representations have the form of functional relations between a set of variables. One major challenge of quantitative modeling consists in constructing valid measures for these variables. Formally, to measure a variable means to construct a numerical representation of the underlying empirical relational structure (Krantz et al., 1971 ). For example, take the behaviors of a group of students in a classroom: “to listen,” “to take notes,” and “to ask critical questions.” One may now ask whether is possible to assign numbers to the students, such that the relations between the assigned numbers are of the same kind as the relations between the values of an underlying variable, like e.g., “engagement.” The observed behaviors in the classroom constitute an empirical relational structure, in the sense that for every student-behavior tuple, one can observe whether it is true or not. These observations can be represented in a person × behavior matrix 1 (compare Figure 1 ). Given this relational structure satisfies certain conditions (i.e., the axioms of a measurement model), one can assign numbers to the students and the behaviors, such that the relations between the numbers resemble the corresponding numerical relations. For example, if there is a unique ordering in the empirical observations with regard to which person shows which behavior, the assigned numbers have to constitute a corresponding unique ordering, as well. Such an ordering coincides with the person × behavior matrix forming a triangle shaped relation and is formally represented by a Guttman scale (Guttman, 1944 ). There are various measurement models available for different empirical structures (Suppes et al., 1971 ). In the case of probabilistic relations, Item-Response models may be considered as a special kind of measurement model (Borsboom, 2005 ).

Constructing a numerical representation from an empirical relational structure; Due to the unique ordering of persons with regard to behaviors (indicated by the triangular shape of the relation), it is possible to construct a Guttman scale by assigning a number to each of the individuals, representing the number of relevant behaviors shown by the individual. The resulting variable (“engagement”) can then be described by means of statistical analyses, like, e.g., plotting the frequency distribution.

Although essential, measurement is only the first step of quantitative modeling. Consider a slightly richer empirical structure, where we observe three additional behaviors: “to doodle,” “to chat,” and “to play.” Like above, one may ask, whether there is a unique ordering of the students with regard to these behaviors that can be represented by an underlying variable (i.e., whether the matrix forms a Guttman scale). If this is the case, we may assign corresponding numbers to the students and call this variable “distraction.” In our example, such a representation is possible. We can thus assign two numbers to each student, one representing his or her “engagement” and one representing his or her “distraction” (compare Figure 2 ). These measurements can now be used to construct a quantitative model by relating the two variables by a mathematical function. In the simplest case, this may be a linear function. This functional relation constitutes a quantitative model of the empirical relational structure under study (like, e.g., linear regression). Given the model equation and the rules for assigning the numbers (i.e., the instrumentations of the two variables), the set of admissible empirical structures is limited from all possible structures to a rather small subset. This constitutes the empirical content of the model 2 (Popper, 1935 ).

Constructing a numerical model from an empirical relational structure; Since there are two distinct classes of behaviors that each form a Guttman scale, it is possible to assign two numbers to each individual, correspondingly. The resulting variables (“engagement” and “distraction”) can then be related by a mathematical function, which is indicated by the scatterplot and red line on the right hand side.

The Qualitative Strategy–Categories and Typologies

The predominant type of analysis in qualitative research consists in category formation. By constructing descriptive systems for empirical phenomena, it is possible to analyze the underlying empirical structure at a higher level of abstraction. The resulting categories (or types) constitute a conceptual frame for the interpretation of the observations. Qualitative researchers differ considerably in the way they collect and analyze data (Miles et al., 2014 ). However, despite the diverse research strategies followed by different qualitative methodologies, from a formal perspective, most approaches build on some kind of categorization of cases that share some common features. The process of category formation is essential in many qualitative methodologies, like, for example, qualitative content analysis, thematic analysis, grounded theory (see Flick, 2014 for an overview). Sometimes these features are directly observable (like in our classroom example), sometimes they are themselves the result of an interpretative process (e.g., Scheunpflug et al., 2016 ).

In contrast to quantitative methodologies, there have been little attempts to formalize qualitative research strategies (compare, however, Rihoux and Ragin, 2009 ). However, there are several statistical approaches to non-numerical data that deal with constructing abstract categories and establishing relations between these categories (Agresti, 2013 ). Some of these methods are very similar to qualitative category formation on a conceptual level. For example, cluster analysis groups cases into homogenous categories (clusters) based on their similarity on a distance metric.

Although category formation can be formalized in a mathematically rigorous way (Ganter and Wille, 1999 ), qualitative research hardly acknowledges these approaches. 3 However, in order to find a common ground with quantitative science, it is certainly helpful to provide a formal interpretation of category systems.

Let us reconsider the above example of students in a classroom. The quantitative strategy was to assign numbers to the students with regard to variables and to relate these variables via a mathematical function. We can analyze the same empirical structure by grouping the behaviors to form abstract categories. If the aim is to construct an empirically valid category system, this grouping is subject to constraints, analogous to those used to specify a measurement model. The first and most important constraint is that the behaviors must form equivalence classes, i.e., within categories, behaviors need to be equivalent, and across categories, they need to be distinct (formally, the relational structure must obey the axioms of an equivalence relation). When objects are grouped into equivalence classes, it is essential to specify the criterion for empirical equivalence. In qualitative methodology, this is sometimes referred to as the tertium comparationis (Flick, 2014 ). One possible criterion is to group behaviors such that they constitute a set of specific common attributes of a group of people. In our example, we might group the behaviors “to listen,” “to take notes,” and “to doodle,” because these behaviors are common to the cases B, C, and D, and they are also specific for these cases, because no other person shows this particular combination of behaviors. The set of common behaviors then forms an abstract concept (e.g., “moderate distraction”), while the set of persons that show this configuration form a type (e.g., “the silent dreamer”). Formally, this means to identify the maximal rectangles in the underlying empirical relational structure (see Figure 3 ). This procedure is very similar to the way we constructed a Guttman scale, the only difference being that we now use different aspects of the empirical relational structure. 4 In fact, the set of maximal rectangles can be determined by an automated algorithm (Ganter, 2010 ), just like the dimensionality of an empirical structure can be explored by psychometric scaling methods. Consequently, we can identify the empirical content of a category system or a typology as the set of empirical structures that conforms to it. 5 Whereas the quantitative strategy was to search for scalable sub-matrices and then relate the constructed variables by a mathematical function, the qualitative strategy is to construct an empirical typology by grouping cases based on their specific similarities. These types can then be related to one another by a conceptual model that describes their semantic and empirical overlap (see Figure 3 , right hand side).

Constructing a conceptual model from an empirical relational structure; Individual behaviors are grouped to form abstract types based on them being shared among a specific subset of the cases. Each type constitutes a set of specific commonalities of a class of individuals (this is indicated by the rectangles on the left hand side). The resulting types (“active learner,” “silent dreamer,” “distracted listener,” and “troublemaker”) can then be related to one another to explicate their semantic and empirical overlap, as indicated by the Venn-diagram on the right hand side.

Variable-Based Models and Case-Based Models

In the previous section, we have argued that qualitative category formation and quantitative measurement can both be characterized as methods to construct abstract representations of empirical relational structures. Instead of focusing on different philosophical approaches to empirical science, we tried to stress the formal similarities between both approaches. However, it is worth also exploring the dissimilarities from a formal perspective.

Following the above analysis, the quantitative approach can be characterized by the use of variable-based models, whereas the qualitative approach is characterized by case-based models (Ragin, 1987 ). Formally, we can identify the rows of an empirical person × behavior matrix with a person-space, and the columns with a corresponding behavior-space. A variable-based model abstracts from the single individuals in a person-space to describe the structure of behaviors on a population level. A case-based model, on the contrary, abstracts from the single behaviors in a behavior-space to describe individual case configurations on the level of abstract categories (see Table 1 ).

Variable-based models and case-based models.

From a representational perspective, there is no a priori reason to favor one type of model over the other. Both approaches provide different analytical tools to construct an abstract representation of an empirical relational structure. However, since the two modeling approaches make use of different information (person-space vs. behavior-space), this comes with some important implications for the researcher employing one of the two strategies. These are concerned with the role of deductive and inductive reasoning.

In variable-based models, empirical structures are represented by functional relations between variables. These are usually stated as scientific laws (Carnap, 1928 ). Formally, these laws correspond to logical expressions of the form

In plain text, this means that y is a function of x for all objects i in the relational structure under consideration. For example, in the above example, one may formulate the following law: for all students in the classroom it holds that “distraction” is a monotone decreasing function of “engagement.” Such a law can be used to derive predictions for single individuals by means of logical deduction: if the above law applies to all students in the classroom, it is possible to calculate the expected distraction from a student's engagement. An empirical observation can now be evaluated against this prediction. If the prediction turns out to be false, the law can be refuted based on the principle of falsification (Popper, 1935 ). If a scientific law repeatedly withstands such empirical tests, it may be considered to be valid with regard to the relational structure under consideration.

In case-based models, there are no laws about a population, because the model does not abstract from the cases but from the observed behaviors. A case-based model describes the underlying structure in terms of existential sentences. Formally, this corresponds to a logical expression of the form

In plain text, this means that there is at least one case i for which the condition XYZ holds. For example, the above category system implies that there is at least one active learner. This is a statement about a singular observation. It is impossible to deduce a statement about another person from an existential sentence like this. Therefore, the strategy of falsification cannot be applied to test the model's validity in a specific context. If one wishes to generalize to other cases, this is accomplished by inductive reasoning, instead. If we observed one person that fulfills the criteria of calling him or her an active learner, we can hypothesize that there may be other persons that are identical to the observed case in this respect. However, we do not arrive at this conclusion by logical deduction, but by induction.

Despite this important distinction, it would be wrong to conclude that variable-based models are intrinsically deductive and case-based models are intrinsically inductive. 6 Both types of reasoning apply to both types of models, but on different levels. Based on a person-space, in a variable-based model one can use deduction to derive statements about individual persons from abstract population laws. There is an analogous way of reasoning for case-based models: because they are based on a behavior space, it is possible to deduce statements about singular behaviors. For example, if we know that Peter is an active learner, we can deduce that he takes notes in the classroom. This kind of deductive reasoning can also be applied on a higher level of abstraction to deduce thematic categories from theoretical assumptions (Braun and Clarke, 2006 ). Similarly, there is an analog for inductive generalization from the perspective of variable-based modeling: since the laws are only quantified over the person-space, generalizations to other behaviors rely on inductive reasoning. For example, it is plausible to assume that highly engaged students tend to do their homework properly–however, in our example this behavior has never been observed. Hence, in variable-based models we usually generalize to other behaviors by means of induction. This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains.

Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases. A variable-based approach implies to draw conclusions about cases by means of logical deduction; a case-based approach implies to draw conclusions about cases by means of inductive reasoning. In the following, we build on this distinction to differentiate between qualitative (bottom-up) and quantitative (top-down) strategies of generalization.

Generalization and the Problem of Replication

We will now extend the formal analysis of quantitative and qualitative approaches to the question of generalization and replicability of empirical findings. For this sake, we have to introduce some concepts of formal logic. Formal logic is concerned with the validity of arguments. It provides conditions to evaluate whether certain sentences (conclusions) can be derived from other sentences (premises). In this context, a theory is nothing but a set of sentences (also called axioms). Formal logic provides tools to derive new sentences that must be true, given the axioms are true (Smith, 2020 ). These derived sentences are called theorems or, in the context of empirical science, predictions or hypotheses . On the syntactic level, the rules of logic only state how to evaluate the truth of a sentence relative to its premises. Whether or not sentences are actually true, is formally specified by logical semantics.

On the semantic level, formal logic is intrinsically linked to set-theory. For example, a logical statement like “all dogs are mammals,” is true if and only if the set of dogs is a subset of the set of mammals. Similarly, the sentence “all chatting students doodle” is true if and only if the set of chatting students is a subset of the set of doodling students (compare Figure 3 ). Whereas, the first sentence is analytically true due to the way we define the words “dog” and “mammal,” the latter can be either true or false, depending on the relational structure we actually observe. We can thus interpret an empirical relational structure as the truth criterion of a scientific theory. From a logical point of view, this corresponds to the semantics of a theory. As shown above, variable-based and case-based models both give a formal representation of the same kinds of empirical structures. Accordingly, both types of models can be stated as formal theories. In the variable-based approach, this corresponds to a set of scientific laws that are quantified over the members of an abstract population (these are the axioms of the theory). In the case-based approach, this corresponds to a set of abstract existential statements about a specific class of individuals.

In contrast to mathematical axiom systems, empirical theories are usually not considered to be necessarily true. This means that even if we find no evidence against a theory, it is still possible that it is actually wrong. We may know that a theory is valid in some contexts, yet it may fail when applied to a new set of behaviors (e.g., if we use a different instrumentation to measure a variable) or a new population (e.g., if we draw a new sample).

From a logical perspective, the possibility that a theory may turn out to be false stems from the problem of contingency . A statement is contingent, if it is both, possibly true and possibly false. Formally, we introduce two modal operators: □ to designate logical necessity, and ◇ to designate logical possibility. Semantically, these operators are very similar to the existential quantifier, ∃, and the universal quantifier, ∀. Whereas ∃ and ∀ refer to the individual objects within one relational structure, the modal operators □ and ◇ range over so-called possible worlds : a statement is possibly true, if and only if it is true in at least one accessible possible world, and a statement is necessarily true if and only if it is true in every accessible possible world (Hughes and Cresswell, 1996 ). Logically, possible worlds are mathematical abstractions, each consisting of a relational structure. Taken together, the relational structures of all accessible possible worlds constitute the formal semantics of necessity, possibility and contingency. 7

In the context of an empirical theory, each possible world may be identified with an empirical relational structure like the above classroom example. Given the set of intended applications of a theory (the scope of the theory, one may say), we can now construct possible world semantics for an empirical theory: each intended application of the theory corresponds to a possible world. For example, a quantified sentence like “all chatting students doodle” may be true in one classroom and false in another one. In terms of possible worlds, this would correspond to a statement of contingency: “it is possible that all chatting students doodle in one classroom, and it is possible that they don't in another classroom.” Note that in the above expression, “all students” refers to the students in only one possible world, whereas “it is possible” refers to the fact that there is at least one possible world for each of the specified cases.

To apply these possible world semantics to quantitative research, let us reconsider how generalization to other cases works in variable-based models. Due to the syntactic structure of quantitative laws, we can deduce predictions for singular observations from an expression of the form ∀ i : y i = f ( x i ). Formally, the logical quantifier ∀ ranges only over the objects of the corresponding empirical relational structure (in our example this would refer to the students in the observed classroom). But what if we want to generalize beyond the empirical structure we actually observed? The standard procedure is to assume an infinitely large, abstract population from which a random sample is drawn. Given the truth of the theory, we can deduce predictions about what we may observe in the sample. Since usually we deal with probabilistic models, we can evaluate our theory by means of the conditional probability of the observations, given the theory holds. This concept of conditional probability is the foundation of statistical significance tests (Hogg et al., 2013 ), as well as Bayesian estimation (Watanabe, 2018 ). In terms of possible world semantics, the random sampling model implies that all possible worlds (i.e., all intended applications) can be conceived as empirical sub-structures from a greater population structure. For example, the empirical relational structure constituted by the observed behaviors in a classroom would be conceived as a sub-matrix of the population person × behavior matrix. It follows that, if a scientific law is true in the population, it will be true in all possible worlds, i.e., it will be necessarily true. Formally, this corresponds to an expression of the form

The statistical generalization model thus constitutes a top-down strategy for dealing with individual contexts that is analogous to the way variable-based models are applied to individual cases (compare Table 1 ). Consequently, if we apply a variable-based model to a new context and find out that it does not fit the data (i.e., there is a statistically significant deviation from the model predictions), we have reason to doubt the validity of the theory. This is what makes the problem of low replicability so important: we observe that the predictions are wrong in a new study; and because we apply a top-down strategy of generalization to contexts beyond the ones we observed, we see our whole theory at stake.

Qualitative research, on the contrary, follows a different strategy of generalization. Since case-based models are formulated by a set of context-specific existential sentences, there is no need for universal truth or necessity. In contrast to statistical generalization to other cases by means of random sampling from an abstract population, the usual strategy in case-based modeling is to employ a bottom-up strategy of generalization that is analogous to the way case-based models are applied to individual cases. Formally, this may be expressed by stating that the observed qualia exist in at least one possible world, i.e., the theory is possibly true:

This statement is analogous to the way we apply case-based models to individual cases (compare Table 1 ). Consequently, the set of intended applications of the theory does not follow from a sampling model, but from theoretical assumptions about which cases may be similar to the observed cases with respect to certain relevant characteristics. For example, if we observe that certain behaviors occur together in one classroom, following a bottom-up strategy of generalization, we will hypothesize why this might be the case. If we do not replicate this finding in another context, this does not question the model itself, since it was a context-specific theory all along. Instead, we will revise our hypothetical assumptions about why the new context is apparently less similar to the first one than we originally thought. Therefore, if an empirical finding does not replicate, we are more concerned about our understanding of the cases than about the validity of our theory.

Whereas statistical generalization provides us with a formal (and thus somehow more objective) apparatus to evaluate the universal validity of our theories, the bottom-up strategy forces us to think about the class of intended applications on theoretical grounds. This means that we have to ask: what are the boundary conditions of our theory? In the above classroom example, following a bottom-up strategy, we would build on our preliminary understanding of the cases in one context (e.g., a public school) to search for similar and contrasting cases in other contexts (e.g., a private school). We would then re-evaluate our theoretical description of the data and explore what makes cases similar or dissimilar with regard to our theory. This enables us to expand the class of intended applications alongside with the theory.

Of course, none of these strategies is superior per se . Nevertheless, they rely on different assumptions and may thus be more or less adequate in different contexts. The statistical strategy relies on the assumption of a universal population and invariant measurements. This means, we assume that (a) all samples are drawn from the same population and (b) all variables refer to the same behavioral classes. If these assumptions are true, statistical generalization is valid and therefore provides a valuable tool for the testing of empirical theories. The bottom-up strategy of generalization relies on the idea that contexts may be classified as being more or less similar based on characteristics that are not part of the model being evaluated. If such a similarity relation across contexts is feasible, the bottom-up strategy is valid, as well. Depending on the strategy of generalization, replication of empirical research serves two very different purposes. Following the (top-down) principle of generalization by deduction from scientific laws, replications are empirical tests of the theory itself, and failed replications question the theory on a fundamental level. Following the (bottom-up) principle of generalization by induction to similar contexts, replications are a means to explore the boundary conditions of a theory. Consequently, failed replications question the scope of the theory and help to shape the set of intended applications.

We have argued that quantitative and qualitative research are best understood by means of the structure of the employed models. Quantitative science mainly relies on variable-based models and usually employs a top-down strategy of generalization from an abstract population to individual cases. Qualitative science prefers case-based models and usually employs a bottom-up strategy of generalization. We further showed that failed replications have very different implications depending on the underlying strategy of generalization. Whereas in the top-down strategy, replications are used to test the universal validity of a model, in the bottom-up strategy, replications are used to explore the scope of a model. We will now address the implications of this analysis for psychological research with regard to the problem of replicability.

Modern day psychology almost exclusively follows a top-down strategy of generalization. Given the quantitative background of most psychological theories, this is hardly surprising. Following the general structure of variable-based models, the individual case is not the focus of the analysis. Instead, scientific laws are stated on the level of an abstract population. Therefore, when applying the theory to a new context, a statistical sampling model seems to be the natural consequence. However, this is not the only possible strategy. From a logical point of view, there is no reason to assume that a quantitative law like ∀ i : y i = f ( x i ) implies that the law is necessarily true, i.e.,: □(∀ i : y i = f ( x i )). Instead, one might just as well define the scope of the theory following an inductive strategy. 8 Formally, this would correspond to the assumption that the observed law is possibly true, i.e.,: ◇(∀ i : y i = f ( x i )). For example, we may discover a functional relation between “engagement” and “distraction” without referring to an abstract universal population of students. Instead, we may hypothesize under which conditions this functional relation may be valid and use these assumptions to inductively generalize to other cases.

If we take this seriously, this would require us to specify the intended applications of the theory: in which contexts do we expect the theory to hold? Or, equivalently, what are the boundary conditions of the theory? These boundary conditions may be specified either intensionally, i.e., by giving external criteria for contexts being similar enough to the ones already studied to expect a successful application of the theory. Or they may be specified extensionally, by enumerating the contexts where the theory has already been shown to be valid. These boundary conditions need not be restricted to the population we refer to, but include all kinds of contextual factors. Therefore, adopting a bottom-up strategy, we are forced to think about these factors and make them an integral part of our theories.

In fact, there is good reason to believe that bottom-up generalization may be more adequate in many psychological studies. Apart from the pitfalls associated with statistical generalization that have been extensively discussed in recent years (e.g., p-hacking, underpowered studies, publication bias), it is worth reflecting on whether the underlying assumptions are met in a particular context. For example, many samples used in experimental psychology are not randomly drawn from a large population, but are convenience samples. If we use statistical models with non-random samples, we have to assume that the observations vary as if drawn from a random sample. This may indeed be the case for randomized experiments, because all variation between the experimental conditions apart from the independent variable will be random due to the randomization procedure. In this case, a classical significance test may be regarded as an approximation to a randomization test (Edgington and Onghena, 2007 ). However, if we interpret a significance test as an approximate randomization test, we test not for generalization but for internal validity. Hence, even if we use statistical significance tests when assumptions about random sampling are violated, we still have to use a different strategy of generalization. This issue has been discussed in the context of small-N studies, where variable-based models are applied to very small samples, sometimes consisting of only one individual (Dugard et al., 2012 ). The bottom-up strategy of generalization that is employed by qualitative researchers, provides such an alternative.

Another important issue in this context is the question of measurement invariance. If we construct a variable-based model in one context, the variables refer to those behaviors that constitute the underlying empirical relational structure. For example, we may construct an abstract measure of “distraction” using the observed behaviors in a certain context. We will then use the term “distraction” as a theoretical term referring to the variable we have just constructed to represent the underlying empirical relational structure. Let us now imagine we apply this theory to a new context. Even if the individuals in our new context are part of the same population, we may still get into trouble if the observed behaviors differ from those used in the original study. How do we know whether these behaviors constitute the same variable? We have to ensure that in any new context, our measures are valid for the variables in our theory. Without a proper measurement model, this will be hard to achieve (Buntins et al., 2017 ). Again, we are faced with the necessity to think of the boundary conditions of our theories. In which contexts (i.e., for which sets of individuals and behaviors) do we expect our theory to work?

If we follow the rationale of inductive generalization, we can explore the boundary conditions of a theory with every new empirical study. We thus widen the scope of our theory by comparing successful applications in different contexts and unsuccessful applications in similar contexts. This may ultimately lead to a more general theory, maybe even one of universal scope. However, unless we have such a general theory, we might be better off, if we treat unsuccessful replications not as a sign of failure, but as a chance to learn.

Author Contributions

MB conceived the original idea and wrote the first draft of the paper. MS helped to further elaborate and scrutinize the arguments. All authors contributed to the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Annette Scheunpflug for helpful comments on an earlier version of the manuscript.

1 A person × behavior matrix constitutes a very simple relational structure that is common in psychological research. This is why it is chosen here as a minimal example. However, more complex structures are possible, e.g., by relating individuals to behaviors over time, with individuals nested within groups etc. For a systematic overview, compare Coombs ( 1964 ).

2 This notion of empirical content applies only to deterministic models. The empirical content of a probabilistic model consists in the probability distribution over all possible empirical structures.

3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick ( 2014 ) nor the Oxford Handbook of Qualitative Research edited by Leavy ( 2014 ) mention formal approaches to category formation.

4 Note also that the described structure is empirically richer than a nominal scale. Therefore, a reduction of qualitative category formation to be a special (and somehow trivial) kind of measurement is not adequate.

5 It is possible to extend this notion of empirical content to the probabilistic case (this would correspond to applying a latent class analysis). But, since qualitative research usually does not rely on formal algorithms (neither deterministic nor probabilistic), there is currently little practical use of such a concept.

6 We do not elaborate on abductive reasoning here, since, given an empirical relational structure, the concept can be applied to both types of models in the same way (Schurz, 2008 ). One could argue that the underlying relational structure is not given a priori but has to be constructed by the researcher and will itself be influenced by theoretical expectations. Therefore, abductive reasoning may be necessary to establish an empirical relational structure in the first place.

7 We shall not elaborate on the metaphysical meaning of possible worlds here, since we are only concerned with empirical theories [but see Tooley ( 1999 ), for an overview].

8 Of course, this also means that it would be equally reasonable to employ a top-down strategy of generalization using a case-based model by postulating that □(∃ i : XYZ i ). The implications for case-based models are certainly worth exploring, but lie beyond the scope of this article.

• Agresti A. (2013). Categorical Data Analysis, 3rd Edn. Wiley Series In Probability And Statistics . Hoboken, NJ: Wiley. [ Google Scholar ]
• Borsboom D. (2005). Measuring the Mind: Conceptual Issues in Contemporary Psychometrics . Cambridge: Cambridge University Press; 10.1017/CBO9780511490026 [ CrossRef ] [ Google Scholar ]
• Braun V., Clarke V. (2006). Using thematic analysis in psychology . Qual. Res. Psychol . 3 , 77–101. 10.1191/1478088706qp063oa [ CrossRef ] [ Google Scholar ]
• Buntins M., Buntins K., Eggert F. (2017). Clarifying the concept of validity: from measurement to everyday language . Theory Psychol. 27 , 703–710. 10.1177/0959354317702256 [ CrossRef ] [ Google Scholar ]
• Carnap R. (1928). The Logical Structure of the World . Berkeley, CA: University of California Press. [ Google Scholar ]
• Coombs C. H. (1964). A Theory of Data . New York, NY: Wiley. [ Google Scholar ]
• Creswell J. W. (2015). A Concise Introduction to Mixed Methods Research . Los Angeles, CA: Sage. [ Google Scholar ]
• Dugard P., File P., Todman J. B. (2012). Single-Case and Small-N Experimental Designs: A Practical Guide to Randomization Tests 2nd Edn . New York, NY: Routledge; 10.4324/9780203180938 [ CrossRef ] [ Google Scholar ]
• Edgington E., Onghena P. (2007). Randomization Tests, 4th Edn. Statistics. Hoboken, NJ: CRC Press; 10.1201/9781420011814 [ CrossRef ] [ Google Scholar ]
• Everett J. A. C., Earp B. D. (2015). A tragedy of the (academic) commons: interpreting the replication crisis in psychology as a social dilemma for early-career researchers . Front. Psychol . 6 :1152. 10.3389/fpsyg.2015.01152 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Flick U. (Ed.). (2014). The Sage Handbook of Qualitative Data Analysis . London: Sage; 10.4135/9781446282243 [ CrossRef ] [ Google Scholar ]
• Freeman M., Demarrais K., Preissle J., Roulston K., St. Pierre E. A. (2007). Standards of evidence in qualitative research: an incitement to discourse . Educ. Res. 36 , 25–32. 10.3102/0013189X06298009 [ CrossRef ] [ Google Scholar ]
• Ganter B. (2010). Two basic algorithms in concept analysis , in Lecture Notes In Computer Science. Formal Concept Analysis, Vol. 5986 , eds Hutchison D., Kanade T., Kittler J., Kleinberg J. M., Mattern F., Mitchell J. C., et al. (Berlin, Heidelberg: Springer Berlin Heidelberg; ), 312–340. 10.1007/978-3-642-11928-6_22 [ CrossRef ] [ Google Scholar ]
• Ganter B., Wille R. (1999). Formal Concept Analysis . Berlin, Heidelberg: Springer Berlin Heidelberg; 10.1007/978-3-642-59830-2 [ CrossRef ] [ Google Scholar ]
• Guttman L. (1944). A basis for scaling qualitative data . Am. Sociol. Rev . 9 :139 10.2307/2086306 [ CrossRef ] [ Google Scholar ]
• Hogg R. V., Mckean J. W., Craig A. T. (2013). Introduction to Mathematical Statistics, 7th Edn . Boston, MA: Pearson. [ Google Scholar ]
• Hughes G. E., Cresswell M. J. (1996). A New Introduction To Modal Logic . London; New York, NY: Routledge; 10.4324/9780203290644 [ CrossRef ] [ Google Scholar ]
• Klein R. A., Ratliff K. A., Vianello M., Adams R. B., Bahník Š., Bernstein M. J., et al. (2014). Investigating variation in replicability . Soc. Psychol. 45 , 142–152. 10.1027/1864-9335/a000178 [ CrossRef ] [ Google Scholar ]
• Krantz D. H., Luce D., Suppes P., Tversky A. (1971). Foundations of Measurement Volume I: Additive And Polynomial Representations . New York, NY; London: Academic Press; 10.1016/B978-0-12-425401-5.50011-8 [ CrossRef ] [ Google Scholar ]
• Leavy P. (2014). The Oxford Handbook of Qualitative Research . New York, NY: Oxford University Press; 10.1093/oxfordhb/9780199811755.001.0001 [ CrossRef ] [ Google Scholar ]
• Maxwell S. E., Lau M. Y., Howard G. S. (2015). Is psychology suffering from a replication crisis? what does “failure to replicate” really mean? Am. Psychol. 70 , 487–498. 10.1037/a0039400 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miles M. B., Huberman A. M., Saldaña J. (2014). Qualitative Data Analysis: A Methods Sourcebook, 3rd Edn . Los Angeles, CA; London; New Delhi; Singapore; Washington, DC: Sage. [ Google Scholar ]
• Open Science, Collaboration (2015). Estimating the reproducibility of psychological science . Science 349 :Aac4716. 10.1126/science.aac4716 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Popper K. (1935). Logik Der Forschung . Wien: Springer; 10.1007/978-3-7091-4177-9 [ CrossRef ] [ Google Scholar ]
• Ragin C. (1987). The Comparative Method : Moving Beyond Qualitative and Quantitative Strategies . Berkeley, CA: University Of California Press. [ Google Scholar ]
• Rihoux B., Ragin C. (2009). Configurational Comparative Methods: Qualitative Comparative Analysis (Qca) And Related Techniques . Thousand Oaks, CA: Sage Publications, Inc; 10.4135/9781452226569 [ CrossRef ] [ Google Scholar ]
• Scheunpflug A., Krogull S., Franz J. (2016). Understanding learning in world society: qualitative reconstructive research in global learning and learning for sustainability . Int. Journal Dev. Educ. Glob. Learn. 7 , 6–23. 10.18546/IJDEGL.07.3.02 [ CrossRef ] [ Google Scholar ]
• Schurz G. (2008). Patterns of abduction . Synthese 164 , 201–234. 10.1007/s11229-007-9223-4 [ CrossRef ] [ Google Scholar ]
• Shrout P. E., Rodgers J. L. (2018). Psychology, science, and knowledge construction: broadening perspectives from the replication crisis . Annu. Rev. Psychol . 69 , 487–510. 10.1146/annurev-psych-122216-011845 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith P. (2020). An Introduction To Formal Logic . Cambridge: Cambridge University Press. 10.1017/9781108328999 [ CrossRef ] [ Google Scholar ]
• Suppes P., Krantz D. H., Luce D., Tversky A. (1971). Foundations of Measurement Volume II: Geometrical, Threshold, and Probabilistic Representations . New York, NY; London: Academic Press. [ Google Scholar ]
• Tooley M. (Ed.). (1999). Necessity and Possibility. The Metaphysics of Modality . New York, NY; London: Garland Publishing. [ Google Scholar ]
• Trafimow D. (2018). An a priori solution to the replication crisis . Philos. Psychol . 31 , 1188–1214. 10.1080/09515089.2018.1490707 [ CrossRef ] [ Google Scholar ]
• Watanabe S. (2018). Mathematical Foundations of Bayesian Statistics. CRC Monographs On Statistics And Applied Probability . Boca Raton, FL: Chapman And Hall. [ Google Scholar ]
• Wiggins B. J., Chrisopherson C. D. (2019). The replication crisis in psychology: an overview for theoretical and philosophical psychology . J. Theor. Philos. Psychol. 39 , 202–217. 10.1037/teo0000137 [ CrossRef ] [ Google Scholar ]