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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BSc (Hons) Psychology, MSc Psychology of Education

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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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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.

Qualitative vs Quantitative Research Explained: Data & Analysis

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…

Qualitative research helps you understand people's perceptions and experiences by systematically analysing the data.

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 and quantitative data analysis in research

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.

Your research approach should be chosen based on your broader research aims, objectives and research questions.

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 research often features small sample sizes, whereas quantitative research relies on large, representative samples.

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 benefits

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.

Qualitative research can suffer from low generalisability, researcher bias, and  can take a long time to execute well.

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.

Quantitative research is typically considered to be more objective, quicker to execute and provides greater anonymity to respondents.

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.

Quantitative research can be comparatively reductive - in other words, it can lead to an oversimplification of a situation.

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.

qualitative and quantitative data analysis in research

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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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  • 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.

Qualitative vs quantitative research

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analysing quantitative data

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

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

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

Analysing qualitative data

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

Some common approaches to analysing qualitative data include:

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

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

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

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

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

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

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

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

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

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

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Raimo Streefkerk

Raimo Streefkerk

Quantitative vs Qualitative Data: What’s the Difference?

If you’re considering a career in data—or in any kind of research field, like psychology—you’ll need to get to grips with two types of data: Quantitative and qualitative .

Quantitative data is anything that can be counted or measured ; it refers to numerical data. Qualitative data is descriptive , referring to things that can be observed but not measured—such as colors or emotions.

In this post, we’ll define both quantitative and qualitative data in more detail. We’ll then explore all the key ways in which they differ—from how they are collected and analyzed, to the advantages and disadvantages of each. We’ll also include useful examples throughout.

By the end, you’ll have a clear understanding of the difference between qualitative and quantitative data, and a good idea of when to use which. Want to skip ahead to a specific section? Just use this clickable menu:

  • Quantitative vs qualitative data: What are they, and what’s the difference between them?
  • What are the different types of quantitative and qualitative data?
  • How are quantitative and qualitative data collected?
  • Quantitative vs qualitative data: Methods of analysis
  • What are the advantages and disadvantages of quantitative vs qualitative data?
  • When should I use qualitative or quantitative data?
  • Quantitative vs. qualitative data: FAQ
  • Key takeaways 

Without further ado, let’s jump in.

1. What is the difference between quantitative and qualitative data?

When it comes to conducting research and data analysis, you’ll work with two types of data: quantitative and qualitative. Each requires different collection and analysis methods, so it’s important to understand the difference between the two.

What is quantitative data?

Quantitative data refers to any information that can be quantified. If it can be counted or measured, and given a numerical value, it’s quantitative data. Quantitative data can tell you “how many,” “how much,” or “how often”—for example, how many people attended last week’s webinar? How much revenue did the company make in 2019? How often does a certain customer group use online banking?

To analyze and make sense of quantitative data, you’ll conduct statistical analyses.

Learn more: What is quantitative data? A complete introduction

What is qualitative data?

Unlike quantitative data, qualitative data cannot be measured or counted. It’s descriptive, expressed in terms of language rather than numerical values.

Researchers will often turn to qualitative data to answer “Why?” or “How?” questions. For example, if your quantitative data tells you that a certain website visitor abandoned their shopping cart three times in one week, you’d probably want to investigate why—and this might involve collecting some form of qualitative data from the user. Perhaps you want to know how a user feels about a particular product; again, qualitative data can provide such insights. In this case, you’re not just looking at numbers; you’re asking the user to tell you, using language, why they did something or how they feel.

Qualitative data also refers to the words or labels used to describe certain characteristics or traits—for example, describing the sky as blue or labeling a particular ice cream flavor as vanilla.

What are the main differences between quantitative and qualitative data?

The main differences between quantitative and qualitative data lie in what they tell us , how they are collected , and how they are analyzed. Let’s summarize the key differences before exploring each aspect in more detail:

  • Quantitative data is countable or measurable, relating to numbers. Qualitative data is descriptive, relating to language.
  • Quantitative data tells us how many, how much, or how often (e.g. “20 people signed up to our email newsletter last week”). Qualitative data can help us to understand the “why” or “how” behind certain behaviors, or it can simply describe a certain attribute—for example, “The postbox is red” or “I signed up to the email newsletter because I’m really interested in hearing about local events.”
  • Quantitative data is fixed and “universal,” while qualitative data is subjective and dynamic. For example, if something weighs 20 kilograms, that can be considered an objective fact. However, two people may have very different qualitative accounts of how they experience a particular event.
  • Quantitative data is gathered by measuring and counting. Qualitative data is collected by interviewing and observing.
  • Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.

The difference between quantitative and qualitative data: An example

To illustrate the difference between quantitative and qualitative data, let’s use an example. Imagine you want to describe your best friend. What kind of data might you gather or use to paint a vivid picture?

First, you might describe their physical attributes, such as their height, their hair style and color, what size feet they have, and how much they weigh. Then you might describe some of their most prominent personality traits. On top of that, you could describe how many siblings and pets they have, where they live, and how often they go swimming (their favorite hobby).

All of that data will fall into either the quantitative or qualitative categories, as follows:

Quantitative data:

  • My best friend is 5 feet and 7 inches tall
  • They have size 6 feet
  • They weigh 63 kilograms
  • My best friend has one older sibling and two younger siblings
  • They have two cats
  • My best friend lives twenty miles away from me
  • They go swimming four times a week

Qualitative data:

  • My best friend has curly brown hair
  • They have green eyes
  • My best friend is funny, loud, and a good listener
  • They can also be quite impatient and impulsive at times
  • My best friend drives a red car
  • They have a very friendly face and a contagious laugh

Of course, when working as a researcher or data analyst, you’ll be handling much more complex data than the examples we’ve given. However, our “best friend” example has hopefully made it easier for you to distinguish between quantitative and qualitative data.

2. Different types of quantitative and qualitative data

When considering the difference between quantitative and qualitative data, it helps to explore some types and examples of each. Let’s do that now, starting with quantitative data.

Types of quantitative data (with examples)

Quantitative data is either discrete or continuous :

  • Discrete quantitative data takes on fixed numerical values and cannot be broken down further. An example of discrete data is when you count something, such as the number of people in a room. If you count 32 people, this is fixed and finite.
  • Continuous quantitative data can be placed on a continuum and infinitely broken down into smaller units. It can take any value; for example, a piece of string can be 20.4cm in length, or the room temperature can be 30.8 degrees.

What are some real-world examples of quantitative data?

Some everyday examples of quantitative data include:

  • Measurements such as height, length, and weight
  • Counts, such as the number of website visitors, sales, or email sign-ups
  • Calculations, such as revenue
  • Projections, such as predicted sales or projected revenue increase expressed as a percentage
  • Quantification of qualitative data—for example, asking customers to rate their satisfaction on a scale of 1-5 and then coming up with an overall customer satisfaction score

Types of qualitative data (with examples)

Qualitative data may be classified as nominal or ordinal :

  • Nominal data is used to label or categorize certain variables without giving them any type of quantitative value. For example, if you were collecting data about your target audience, you might want to know where they live. Are they based in the UK, the USA, Asia, or Australia? Each of these geographical classifications count as nominal data. Another simple example could be the use of labels like “blue,” “brown,” and “green” to describe eye color.
  • Ordinal data is when the categories used to classify your qualitative data fall into a natural order or hierarchy. For example, if you wanted to explore customer satisfaction, you might ask each customer to select whether their experience with your product was “poor,” “satisfactory,” “good,” or “outstanding.” It’s clear that “outstanding” is better than “poor,” but there’s no way of measuring or quantifying the “distance” between the two categories.

Nominal and ordinal data tends to come up within the context of conducting questionnaires and surveys. However, qualitative data is not just limited to labels and categories; it also includes unstructured data such as what people say in an interview, what they write in a product review, or what they post on social media.

What are some real-world examples of qualitative data?

Some examples of qualitative data include:

  • Interview transcripts or audio recordings
  • The text included in an email or social media post
  • Product reviews and customer testimonials
  • Observations and descriptions; e.g. “I noticed that the teacher was wearing a red jumper.”
  • Labels and categories used in surveys and questionnaires, e.g. selecting whether you are satisfied, dissatisfied, or indifferent to a particular product or service.

3. How are quantitative and qualitative data collected?

One of the key differences between quantitative and qualitative data is in how they are collected or generated.

How is quantitative data generated?

Quantitative data is generated by measuring or counting certain entities, or by performing calculations. Some common quantitative data collection methods include:

  • Surveys and questionnaires: This is an especially useful method for gathering large quantities of data. If you wanted to gather quantitative data on employee satisfaction, you might send out a survey asking them to rate various aspects of the organization on a scale of 1-10.
  • Analytics tools: Data analysts and data scientists use specialist tools to gather quantitative data from various sources. For example, Google Analytics gathers data in real-time, allowing you to see, at a glance, all the most important metrics for your website—such as traffic, number of page views, and average session length.
  • Environmental sensors: A sensor is a device which detects changes in the surrounding environment and sends this information to another electronic device, usually a computer. This information is converted into numbers, providing a continuous stream of quantitative data.
  • Manipulation of pre-existing quantitative data: Researchers and analysts will also generate new quantitative data by performing statistical analyses or calculations on existing data. For example, if you have a spreadsheet containing data on the number of sales and expenditures in USD, you could generate new quantitative data by calculating the overall profit margin.

How is qualitative data generated?

Qualitative data is gathered through interviews, surveys, and observations. Let’s take a look at these methods in more detail:

  • Interviews are a great way to learn how people feel about any given topic—be it their opinions on a new product or their experience using a particular service. Conducting interviews will eventually provide you with interview transcripts which can then be analyzed.
  • Surveys and questionnaires are also used to gather qualitative data. If you wanted to collect demographic data about your target audience, you might ask them to complete a survey where they either select their answers from a number of different options, or write their responses as freeform text.
  • Observations: You don’t necessarily have to actively engage with people in order to gather qualitative data. Analysts will also look at “naturally occurring” qualitative data, such as the feedback left in product reviews or what people say in their social media posts.

4. Quantitative vs qualitative data: methods of analysis

Another major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes.

Quantitative data analysis

How you analyze your quantitative data depends on the kind of data you’ve gathered and the insights you want to uncover. Statistical analysis can be used to identify trends in the data, to establish if there’s any kind of relationship between a set of variables (e.g. does social media spend correlate with sales), to calculate probability in order to accurately predict future outcomes, to understand how the data is distributed—and much, much more.

Some of the most popular methods used by data analysts include:

  • Regression analysis
  • Monte Carlo simulation
  • Factor analysis
  • Cohort analysis
  • Cluster analysis
  • Time series analysis

You’ll find a detailed explanation of these methods in our guide to the most useful data analysis techniques .

Qualitative data analysis

With qualitative data analysis, the focus is on making sense of unstructured data (such as large bodies of text). Given that qualitative data cannot be measured objectively, it is open to subjective interpretation and therefore requires a different approach to analysis.

The main method of analysis used with qualitative data is a technique known as thematic analysis. Essentially, the data is coded in order to identify recurring keywords or topics, and then, based on these codes, grouped into meaningful themes.

Another type of analysis is sentiment analysis , which seeks to classify and interpret the emotions conveyed within textual data. This allows businesses to gauge how customers feel about various aspects of the brand, product, or service, and how common these sentiments are across the entire customer base.

Traditionally, qualitative data analysis has had something of a bad reputation for being extremely time-consuming. However, nowadays the process can be largely automated, and there are plenty of tools and software out there to help you make sense of your qualitative data. To learn more about qualitative analysis and what you can do with it, check out this round-up of the most useful qualitative analysis tools on the market .

5. What are the advantages and disadvantages of quantitative vs qualitative data?

Each type of data comes with advantages and disadvantages, and it’s important to bear these in mind when conducting any kind of research or sourcing data for analysis. We’ll outline the main advantages and disadvantages of each now.

What are the advantages and disadvantages of quantitative data?

A big advantage of quantitative data is that it’s relatively quick and easy to collect, meaning you can work with large samples. At the same time, quantitative data is objective; it’s less susceptible to bias than qualitative data, which makes it easier to draw reliable and generalizable conclusions.

The main disadvantage of quantitative data is that it can lack depth and context. The numbers don’t always tell you the full story; for example, you might see that you lost 70% of your newsletter subscribers in one week, but without further investigation, you won’t know why.

What are the advantages and disadvantages of qualitative data?

Where quantitative data falls short, qualitative data shines. The biggest advantage of qualitative data is that it offers rich, in-depth insights and allows you to explore the context surrounding a given topic. Through qualitative data, you can really gauge how people feel and why they take certain actions—crucial if you’re running any kind of organization and want to understand how your target audience operates.

However, qualitative data can be harder and more time-consuming to collect, so you may find yourself working with smaller samples. Because of its subjective nature, qualitative data is also open to interpretation, so it’s important to be aware of bias when conducting qualitative analysis.

6. When should I use qualitative or quantitative data?

Put simply, whether you use qualitative or quantitative data (or a combination of both!) depends on the data analytics project you’re undertaking. Here, we’ll discuss which projects are better suited to which data.

Generally, you can use the following criteria to determine whether to go with qualitative data, quantitative data, or a mixed methods approach to collecting data for your project.

  • Do you want to understand something, such as a concept, experience, or opinions? Use qualitative data.
  • Do you want to confirm or test something, such as a theory or hypothesis? Use quantitative data.
  • Are you taking on research? You may benefit from a mixed methods approach to data collection.

You may find that more often than not, both types of data are used in projects, in order to gain a clear overall image—integrating both the numbers side and human side of things.

6. Quantitative vs. qualitative data: FAQ

What are the main differences between qualitative and quantitative research.

Qualitative research is primarily exploratory and uses non-numerical data to understand underlying reasons, opinions, and motivations. Quantitative research, on the other hand, is numerical and seeks to measure variables and relationships through statistical analysis. Additionally, qualitative research tends to be subjective and less structured, while quantitative research is objective and more structured.

What are examples of qualitative and quantitative data?

Examples of qualitative data include open-ended survey responses, interview transcripts, and observational notes. Examples of quantitative data include numerical survey responses, test scores, and website traffic data. Qualitative data is typically subjective and descriptive, while quantitative data is objective and numerical.

7. Key takeaways

Throughout this post, we’ve defined quantitative and qualitative data and explained how they differ. What it really boils down to, in very simple terms, is that quantitative data is countable or measurable, relating to numbers, while qualitative data is descriptive, relating to language.

Understanding the difference between quantitative and qualitative data is one of the very first steps towards becoming a data expert. If you’re considering a career in data, you’ll find links to some useful articles at the end of this post. Had enough theory and want some action? Check out our list of free data analytics courses for beginners , or cut to the chase and simply sign up for a free, five-day introductory data analytics short course .

  • A step-by-step guide to the data analysis process
  • What is the typical data analyst career path?
  • The best data analytics courses in 2022

qualitative and quantitative data analysis in research

The Ultimate Guide to Qualitative Research - Part 1: The Basics

qualitative and quantitative data analysis in research

  • Introduction and overview
  • What is qualitative research?
  • What is qualitative data?
  • Examples of qualitative data
  • Introduction

Quantitative data

Qualitative data analysis, forms of qualitative data, limitations of qualitative data, how to balance qualitative and quantitative research.

  • Mixed methods
  • Qualitative research preparation
  • Theoretical perspective
  • Theoretical framework
  • Literature reviews
  • Research question
  • Conceptual framework
  • Conceptual vs. theoretical framework
  • Data collection
  • Qualitative research methods
  • Focus groups
  • Observational research
  • Case studies
  • Ethnographical research
  • Ethical considerations
  • Confidentiality and privacy
  • Power dynamics
  • Reflexivity

Qualitative vs. quantitative research: Methods & data analysis

It might be easy to get bogged down in a "qualitative vs. quantitative data" debate, particularly when quantitative and qualitative research seem like very different things. However, both qualitative and quantitative data have their uses in research. Hence, researchers need to know what each approach has to offer before deciding which research approach and methods are best for them.

Over time, your research might rely on both qualitative and quantitative data. It's important not to treat one as more important or better than the other. Instead, it will benefit your research if you know when and how to use both forms of data to address your research inquiries.

qualitative and quantitative data analysis in research

Quantitative data refers to any numerical data that can be used in statistical analysis or experimental research.

Researchers in quantitative research often collect data and conduct analysis to make generalizable conclusions about a particular phenomenon or subject. Survey researchers can sample a portion of a population and assert whether the survey results are indicative of the perspectives of the whole population.

Collecting quantitative data

Generally, quantitative data collection methods are more straightforward than their qualitative data counterparts. Suppose your research question involves measuring foot traffic around a city. In such a project, a researcher could place volunteers at selected places and have them count how many times people cross a street in their view.

The volunteers' counts make the quantitative data needed to answer the research questions. Making assertions about the foot traffic in different places is a relatively simple task, given that the numbers are easily collected and readily available for comparison.

Forms of quantitative data

Quantitative data collection relies on structure and a clear understanding of what the numerical values mean to the research. Quantitative researchers can readily take a spreadsheet of test scores, for example, to generate descriptive statistics and inferential statistics. The shape of that spreadsheet (e.g., rows and columns) and its content (e.g., numerical data) ultimately make analyzing quantitative data feasible.

Limitations of quantitative data

Some phenomena cannot be reduced to mere numbers. For example, quantitative data may tell you the value of a particular product, but it faces significant challenges in helping explain a product's inherent beauty or effectiveness.

Such concepts can be difficult for quantitative data to define. After all, what is beautiful to someone will be less so to someone else, and vice versa.

Quantitative research may also face limitations in measuring people's perspectives. Survey research often relies on Likert scales or rating scales asking respondents to rate something on a numerical scale (e.g., from one to five or one to ten).

However, is one respondent's idea of a "4" on a five-point scale the same as another’s idea of a "4" on this same scale? Moreover, subjective concepts are especially difficult to capture with numerical data.

Qualitative research tends to look at the detail of a phenomenon rather than its numerical value. Qualitative research methods allow for theoretical development or exploration of a relatively unfamiliar phenomenon.

Think about a beautiful song. It might be beautiful because of the melody, singer, lyrics, or perhaps some combination of these and other factors. Collecting quantitative data on each aspect (e.g., "Give the melody of the song a score between one and five") might allow for some statistical analysis of a song.

However, what exactly does someone mean when they give a high rating for a song's melody or lyrics? Do they mean the melody is relaxing, inspiring, or something else? Quantitative approaches alone are insufficient in allowing researchers to determine what people think is a "beautiful melody."

Coding qualitative data

Qualitative research relies on methods like interviews to explore social phenomena beyond the use of numbers. ATLAS.ti lets researchers code qualitative data , summarizing large sets of information more succinctly so that gathering insights becomes easier.

qualitative and quantitative data analysis in research

When someone speaks at length about a song's melody being "relaxing," a researcher can apply the code "relaxing melody" to an entire segment of text in ATLAS.ti. That way, analyzing the data means looking at brief codes instead of lengthy paragraphs or pages where the meaning might be unclear.

Developing theoretical insights

Qualitative analysis can also prompt us to look at a phenomenon from new and different angles. A researcher may conduct in-depth interviews at places where individuals think a song is beautiful, like at a live concert.

The findings may not fit our prior understanding of a beautiful song, meaning quantitative research wouldn't likely capture it. Statistical analysis might have difficulty reaching a reliable conclusion since different people might have different definitions of what makes a beautiful song.

As a result, the potential for qualitative research to further develop theory cannot be understated, particularly when it allows researchers to document new insights that quantitative methods might miss. While the qualitative research process can be daunting, it has the potential to provide more detail than a simple statistical analysis can.

qualitative and quantitative data analysis in research

Put your data to work for you! Quantitative and qualitative data analysis at your fingertips!

Click here for a free trial of ATLAS.ti.

Qualitative studies often draw from the following data collection methods:

  • surveys or questionnaires
  • in-depth interviews
  • focus groups
  • observations
  • document collection

qualitative and quantitative data analysis in research

This is not an exhaustive list, as any unstructured data that can be organized might be considered qualitative data.

What is especially important is that qualitative data is not confined to text. Most forms of information can be analyzed for more insightful discussion. ATLAS.ti allows researchers to code major forms of qualitative data , including images, audio, and video . With the structure provided by coding, researchers can identify recurring themes and patterns in all forms of qualitative data.

qualitative and quantitative data analysis in research

Unlike quantitative data, which is often readily available in spreadsheets, qualitative data tend to lack an easily defined structure that facilitates data analysis . In addition, interpreting non-numerical data can be challenging, while clear formulas exist that researchers can follow to compare quantitative values.

Moreover, in semi-structured interviews or focus groups , researchers may ask follow-up questions that can't easily be predicted. An interesting answer may lead to deeper questions to search for more in-depth insights.

qualitative and quantitative data analysis in research

The need for the interviewer to pursue deeper answers can impede the organization of data into neat rows and columns. However, it is important to organize the data so that different meanings that emerged across participants or data sources can be assessed. Researchers often need to take time to reorganize their data to facilitate interpretation .

Moreover, interpreting non-numerical data is a significant challenge for qualitative researchers. The relative quantitative value of different things can be relatively easy to interpret.

If someone takes the temperature of New York and the temperature of Chicago on the same day and gets two different values, asserting that one city is warmer than the other would be uncontroversial. After all, one need only get a numerical value representing the temperature in each city to come to a fairly straightforward conclusion.

qualitative and quantitative data analysis in research

However, people may disagree about what makes a city interesting or exciting. To take from our example about music, people may even disagree about whether the visual or performative elements of music should be considered. Thus, the researcher needs to clarify the potential differences in understanding between people.

Analyzing qualitative data to answer such research questions requires transparency in analysis. Researchers analyzing socially constructed, subjective concepts should clearly define their concepts so their audiences understand the data analysis.

People can make the mistake of choosing qualitative or quantitative data exclusively. Both approaches are useful in determining cause-and-effect relationships and drawing conclusions based on rigorous analyses.

Choosing research questions

Your inquiry will determine whether quantitative data or qualitative data are more appropriate for your research. In any study, think about how your research question guides what data to collect and how to analyze it.

qualitative and quantitative data analysis in research

A quantitative research question seeks to confirm something based on theory that researchers have already developed. On the other hand, a qualitative research question looks at something unfamiliar for which theory does not yet exist to explain it.

In the end, the research question you ask is more important than deciding whether one approach is generally better than the other. By clearly defining what you want to know, you will have a better understanding of what methods will work best for your research project.

Filling research gaps

Quantitative data collection methods can miss nuances that cannot be measured statistically. In contrast, qualitative data collection methods may lack the necessary precision in research contexts where numerical assessment is required. Ultimately, a multitude of data collection and analysis methods may address your research inquiry better than any singular approach.

In situations where a more comprehensive understanding is required, you may want to consider a mixed methods study that collects and analyzes quantitative and qualitative data. A mixed methods approach that employs both quantitative and qualitative methods can be more time-consuming and cumbersome, but the multiple approaches work hand in hand so that each approach covers the shortcomings of the other.

Advancing the overall research agenda

When choosing whether to collect quantitative data, qualitative data, or both, the bigger question is what you want to know, which determines the data collection methods and data analysis that are most effective for your research project. Researchers can benefit from understanding the strengths and weaknesses of quantitative and qualitative data and deciding how both can benefit their research.

Qualitative vs. quantitative data? ATLAS.ti helps you make sense of both

Download a free trial of ATLAS.ti to see what you can do with your data.

Qualitative vs Quantitative Data: Definitions, Analysis, Examples

If you are involved in statistics, marketing or data science, it is essential to know what is the difference between qualitative and quantitative data and analysis.

On this page:

  • Qualitative vs quantitative data : definition, examples, characteristics, contrast, similarities, and differences.
  • What is quantitative data analysis? Steps and types.
  • What is qualitative data analysis? Steps and types.
  • Comparison chart in PDF (infographic).

What is quantitative data?

Quantitative data seems to be simpler to define and identify.

Quantitative data is data that can be expressed as a number or can be quantified. In other words, quantitative data can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented with a wide variety of statistical types of graphs and chards such as line, graph, bar graph, scatter plot , box and whisker plot and etc.

Key characteristics of quantitative data:

  • It can be quantified and verified.
  • Data can be counted.
  • Data type: number and statistics.
  • It answers questions such as “how many, “how much” and “how often”.

Examples of quantitative data:

  • Scores on tests and exams e.g. 85, 67, 90 and etc.
  • The weight of a person or a subject.
  • The number of hours of study.
  • Your shoe size.
  • The square feet of an apartment.
  • The temperature in a room.
  • The volume of a gas and etc.

Types of quantitative data:

There are 2 general types of quantitative data:

  • Discrete data – a count that involves integers. Only a limited number of values is possible. The discrete values cannot be subdivided into parts. For example, the number of children in a school is discrete data. You can count whole individuals. You can’t count 1.5 kids.
  • Continuous data –  information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value. For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc. More on the topic see in our post discrete vs continuous data .

What is qualitative data?

As you might guess qualitative data is information that can’t be expressed as a number and can’t be measured.

Qualitative data consist of words, pictures, observations, and symbols, not numbers. It is about qualities.

Qualitative data is also called categorical data . The reason is that the information can be sorted by category, not by number. Qualitative data is analyzed to look for common themes.

Key characteristics of qualitative data:

  • It cannot be quantified and verified.
  • Data cannot be counted.
  • Data type: words, objects, pictures, observations, and symbols.
  • It answers questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

  • Your socioeconomic status
  • Colors e.g. the color of the sea
  • The Smell e.g. aromatic, buttery, camphoric and etc.
  • Your favorite holiday destination such as Hawaii, New Zealand and etc.
  • Names as John, Patricia,…..
  • Sounds like bang and blare.
  • Ethnicity such as American Indian, Asian, etc.

Quantitative Data Analysis: Meaning, Steps, and Types

Quantitative data analysis ends with easy to understand and quantifiable results. You can analyze it in many different ways. But before starting the analysis you have to define the level of measurement involved in the quantitative data.

Let’s see the steps in the process of analyzing quantitative variables. It will help us to see better the difference between qualitative and quantitative data analysis.

Step 1: Identify the level of measurement 

There are 4 scales/levels of measurement:

  • Nominal –  data scales used simply for labeling variables, without quantitative value. The nominal data just name a thing without applying it to an order. Even though we can use the numbers, they do not denote quantity. Examples of nominal data: hair color (Blonde, Brown, Brunette, etc.).
  • Ordinal . Ordinal data is placed into some kind of order by their position on the scale. They often indicate superiority. Example of ordinal data: the first, second and third person in a competition. To understand better see our post nominal vs ordinal data .
  • Interval –  numerical scales that show information about an order. In interval scales, the intervals between each data value are the same. A popular example here is the temperature in centigrade, where, for instance, the interval between 930C and 950C is the same as the distance between 1060C and 1080C. However, there isn’t a starting point in the interval scales. See more interval data examples .
  • Ratio –  not only show order and have equal intervals, but they can also have a value of zero.

Identifying the levels of measurement where a dataset falls under, will help you decide whether or not the data is useful in making calculations. The scales of measurement are very important because they determine the types of data analysis that can be performed.

The best way of doing that is with specialized data software.

As you have the raw data, you cannot just sit and look at it. You need to take actions to identify some patterns or to visualize what the data is showing.

This is where descriptive statistics and inferential statistics come to play.

Step 2:  Perform descriptive statistics

Descriptive statistics are used to describes and summarizes basic features of a data set. Commonly used descriptive statistics are:

  • Central tendency (mean, mode, and median).
  • Percentages.
  • Dispersion (range, quartiles, variance, and standard deviation)
  • Distribution.

Step 3:  Perform inferential statistics

Inferential statistics are used to draw conclusions and trends about a large population based on a sample taken from it. Inferential statistics study the relationships between variables within a sample.

Inferential statistics allow you to test different hypotheses and to generalize the gained results to the population as a whole.

Key inferential techniques, methods, and types of calculations are:

  • Linear regression models
  • Logistic regression
  • Analysis of Variance (ANOVA)
  • Analysis of Covariance (ANCOVA)
  • Statistical significance (T-Test)
  • Correlation analysis

Step 4:  Define statistical significance

Finally, you need to look for statistical significance. Statistical significance is captured through a ‘p-value’, which evaluate the probability that your discovering for the data are reliable results, not a coincidence. The lower the p-value, the more confident you can be that your findings are reliable.

As you see when it comes to quantitative data analysis, there are many techniques and methods you can use.

The next step in our post for the difference between qualitative and quantitative data is to see what qualitative data analysis involves.

Qualitative Data Analysis: Definition, steps, and types

It is harder to perform Qualitative Data Analysis (QDA) in comparison with quantitative one.

QDA includes the processes and methods for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data.

Qualitative data analysis is very important because it allows data sciences and statisticians to form parameters for observing and analyzing larger sets of data.

For example, if a company need to identify the diversity of its personnel, it would look at qualitative data such as ethnicity and race of its employees.

For comparison, quantitative data, in this case, could be the frequency of workers to belong to those ethnicities and races.

In general, the qualitative data analysis has the following steps:

Step 1:  Become familiar with your data

As a data scientist or researcher, you have to read and re-read the data, record detailed notes and impressions, and deciding which pieces of data possess value.

Step 2:  Define the key questions that they need to answer through the QDA

Each QDA has specific questions, problems or topics. Find out which questions do you need to answer.

Step 3:   Reduce and code the data into themes

This means to create categories and subcategories. These categories are very likely to get bigger as you work through your data. The list of build themes represents your first set of codes.

Step 4:   Search for patterns and connections

This step involves looking for the relative importance of data, identifying relationships between data sets or themes, and trying to find explanations from the available data.

Step 5:  Interpret the data and draw conclusions

After you identify the themes, connections, and patterns, now you need to assign meaning and importance to the data.

It is very likely to find out much more than you could need, so you will have to decide what is most significant data and results.

Note: qualitative data do not drive conclusions and generalizations across a population. This is an important difference between qualitative and quantitative data analysis. In contrast, quantitative analysis can lead to conclusions or trends about a large population based on a sample taken from it.

There is a range of approaches to qualitative data analysis. Some of the key approaches are:

  • Content analysis – a technique to make inferences by interpreting and coding textual information (e.g., documents, graphics, oral communications).
  • Thematic analysis –  a widely-used QDA method that involves grouping the data into themes to define patterned meaning across a dataset.
  • Discourse analysis – includes a group of approaches for analyzing written or vocal interactions or any significant semiotic event. The method focuses on the social context in which the communication happened. It seeks to understand how people express themselves.
  • Grounded theory – allows you to look for latent social patterns and structures.

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qualitative and quantitative data analysis in research

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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qualitative and quantitative data analysis in research

Data Analysis Methods: Qualitative vs. Quantitative

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Data analysis is a crucial step in extracting meaningful insights from collected data. Two common approaches to analyzing data are qualitative and quantitative analysis. Each method offers different techniques for interpreting and understanding your findings.

This blog post will further explore different qualitative and quantitative analysis methods, their strengths and limitations, and how to apply them in various research and business contexts. Whether you’re a researcher, analyst, or decision maker, understanding these methods will help you make informed decisions when analyzing data and deriving valuable insights.

What is Data Analysis, and Why is it Necessary?

Data analysis is comparable to a detective looking for evidence to uncover important information. It helps us understand trends and patterns in data that we may not see immediately. Analyzing data allows us to make better decisions, find opportunities, and solve problems. It’s necessary because it helps make sense of the large amounts of data available today. Data would be messy and hard to understand without analysis, but we can find connections, discover abnormalities, and understand the bigger picture by analyzing it.

It also helps us predict the future by looking at past data. Historical data is useful in fields like business, finance, and healthcare. It allows us to predict customer behavior, market trends, and potential risks. With this information, we can plan and prepare for what might happen. Data analysis also improves performance and efficiency. By studying data, we can find areas to fix or improve, making things run smoother and using resources wisely.

What Does the Data Analysis Process Entail?

Data analysis involves several key steps to extract meaningful insights from data. Here’s an overview of the typical data analysis process:

  • Objective Definition: Clearly define the objective of the analysis by understanding the specific questions to answer or problems to solve.
  • Data Collection and Preprocessing: Gather relevant data from various sources, ensuring accuracy, completeness, and representativeness. Clean the data by removing errors, inconsistencies, or missing values, and preprocess it as needed (e.g., normalization, standardization).
  • Exploratory Data Analysis (EDA): Explore the data through visualization, charts, graphs, and summary statistics to identify patterns, trends, or relationships and gain initial insights.
  • Data Analysis Techniques: Depending on the data’s objective and characteristics, suitable techniques like descriptive statistics, hypothesis testing, regression, clustering, or classification can be used to analyze data effectively.
  • Interpretation and Communication: Analyze the output of the analysis techniques, interpret the findings in the context of the objective, and draw conclusions. Communicate the results effectively using visualizations, reports, or presentations to stakeholders or decision makers.

Throughout the process, it’s important to validate and verify the analysis by checking for consistency, conducting sensitivity analyses, or using peer review. Additionally, the data analysis process often involves iteration, allowing for refinement and improvement based on initial findings or feedback received.

What is the Difference Between Qualitative and Quantitative Data?

Qualitative and quantitative data are two different types of data used in research and analysis. Here are the key differences between them:

Both qualitative and quantitative data have their strengths and applications. They can be used together in mixed-methods research to comprehensively understand a research topic or triangulate findings for more robust conclusions.

Data Analysis Methods

Data analysis methods refer to the techniques and approaches used to analyze and interpret data. These methods vary depending on the type of data you’re analyzing and the research objectives. Two common categories of data analysis methods are qualitative data analysis and quantitative data analysis.

Qualitative Data

Qualitative data analysis involves examining non-numerical or categorical information to uncover patterns, themes, and meanings. Here are some commonly used methods for analyzing qualitative data:

Thematic Analysis : Identifies recurring themes or patterns in qualitative data by categorizing and coding the data.

Content Analysis : Analyzes textual data systematically by categorizing and coding it to identify patterns and concepts.

Narrative Analysis : Examines stories or narratives to understand experiences, perspectives, and meanings.

Grounded Theory : Develops theories or frameworks based on systematically collected and analyzed data, allowing theory development to be guided by the analysis process.

Quantitative Data

Quantitative data analysis involves analyzing numerical data to uncover statistical patterns, relationships, and trends. Here are some commonly used methods for analyzing quantitative data:

Descriptive Statistics : Summarizes dataset features using mean, median, mode, standard deviation, and percentages.

Inferential Statistics : Draws conclusions about a population based on sample data using hypothesis testing, t-tests, and regression analysis.

Data Mining : Discovers patterns and correlations in large datasets using algorithms and statistical techniques.

Experimental Design : Designs controlled experiments to determine causal relationships between variables.

These are just a few examples of the data analysis methods used for qualitative and quantitative data. The choice of method depends on the research objectives, type of data, available resources, and the specific questions to address. Researchers often employ a combination of methods to comprehensively understand the data and draw meaningful conclusions.

Data Analysis Obstacles

You’ll likely encounter obstacles to obtaining accurate and meaningful insights during the data analysis process. Understanding these obstacles is crucial for effective data analysis. Here are some common ones:

Data Quality Issues: Poor data quality can be a significant obstacle. Addressing data quality issues by carefully cleaning and preprocessing your data is essential.

Insufficient or Unrepresentative Data: If the data collected doesn’t cover the relevant variables or lacks diversity, the insights obtained may be limited or biased. 

Lack of Domain Knowledge: Data analysis often requires domain knowledge to interpret the results accurately. Without a thorough understanding of the subject matter, it can be challenging to identify relevant patterns or relationships in the data.

Complexity and Volume of Data: Large and complex datasets can pose processing, analysis, and interpretation challenges. Analyzing such data requires advanced techniques and tools to handle the volume and complexity effectively.

Biases and Assumptions: Biases and assumptions made during data analysis can influence the process. Biases can occur at various stages, such as data collection, preprocessing, or analysis. 

Overcoming these obstacles requires careful attention to data quality, ensuring representative data, acquiring domain knowledge, utilizing appropriate tools and techniques, and being mindful of biases and assumptions. By addressing these challenges, data analysts can enhance the reliability and validity of their analysis, leading to more accurate and insightful results.

How to Ensure Data Quality

It’s crucial to prioritize data quality to ensure that insights obtained from data analysis are accurate and reliable. Here are some simple steps to ensure data quality:

Data analysis processes

  • Data Collection Planning: Plan the data collection process carefully. Clearly define the data requirements and variables needed to address the analysis objective.
  • Data Cleaning and Validation: Thoroughly clean the collected data to remove errors, inconsistencies, or missing values. Validate the data by cross-checking it against known standards or conducting data verification checks. 
  • Data Standardization: Ensure consistency and comparability by converting data into a common format, unit, or scale. 
  • Data Integration: If working with multiple datasets, integrate them carefully to ensure coherence and accuracy. You should match variables, resolve inconsistencies, and merge all data correctly.
  • Data Documentation: Thoroughly document the data collection and preprocessing procedures. Record data sources, data cleaning steps, transformations applied, and any other modifications made.

By following these steps, data quality can be maintained throughout the analysis process. High-quality data enhances the credibility of the analysis and enables informed decision making based on accurate and trustworthy information.

How Data Analysis Benefits Your Organization

Data analysis offers organizations numerous benefits , helping them improve processes, make informed decisions, and gain a competitive edge. Here are some key advantages of data analysis in clear and simple terms:

Informed Decision Making : Data analysis helps organizations make informed decisions by providing valuable insights and identifying trends and patterns in data.

Improved Efficiency and Productivity : By analyzing data, organizations can identify inefficiencies, streamline processes, and allocate resources effectively, improving efficiency and productivity.

Enhanced Customer Understanding : Data analysis enables organizations to gain a deeper understanding of customers and their needs, preferences, and behavior, enabling personalized marketing strategies and better customer service.

Competitive Advantage : Data analysis helps organizations stay ahead of the competition by identifying market trends, monitoring competitors, and uncovering new opportunities.

Risk Identification and Mitigation : Data analysis allows organizations to identify and mitigate risks by analyzing historical data, detecting potential fraud, predicting customer churn, and proactively developing risk management strategies.

In summary, data analysis empowers organizations to make informed decisions, improve efficiency, understand customers, gain a competitive advantage, and mitigate risks, leading to enhanced performance and success.

Key Takeaways 

Data analysis is a powerful process that offers significant benefits to organizations. It enables organizations to improve efficiency, optimize processes, and allocate resources effectively, leading to cost savings and increased productivity. It also helps organizations better understand their customers, tailor strategies, and develop products that meet customer needs, fostering customer satisfaction and loyalty. Furthermore, data analysis provides a competitive advantage by uncovering market trends, monitoring competitors, and identifying new opportunities.

Check out the following resources to learn even more about data analysis:

  • What is Big Data Analytics?
  • Enterprise Analytics
  • Unstructured Data
  • Semi-Structured Data
  • What is Data Management?
  • What is a Data Platform?
  • Database vs. Data Warehouse: Differences, Use Cases, Examples
  • Couchbase Capella Columnar Adds Real-time Data Analytics Service
  • JSON Analytics Product Page

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Qualitative vs. quantitative data in research: what's the difference?

Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

Qualtitative vs quantitative examples

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.


An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

Fullstory's DXI platform combines the quantitative insights of product analytics with picture-perfect session replay for complete context that helps you answer questions, understand issues, and uncover customer opportunities.

Start a free 14-day trial to see how Fullstory can help you combine your most invaluable quantitative and qualitative insights and eliminate blind spots.

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The Ultimate Guide to Qualitative vs. Quantitative Research

Table of Contents

While working on a research project, we often wonder whether our project is qualitative or quantitative in its approach. Although their objectives and applications overlap in many ways, there are significant differences between them. In this article, we’ll learn about Qualitative vs. Quantitative Research.

What is Qualitative Research?

In qualitative research, different types of non-numerical data is gathered and evaluated to better understand ideas, views, or experiences (such as video, text, or audio). In-depth details about a situation can be discovered or ideas for fresh study concepts can be sparked through it. Quantitative research, which includes gathering and analyzing numerical data for statistical analysis, is the antithesis of qualitative research. The humanities and social sciences frequently employ qualitative research in sociology, anthropology, education, history, health sciences, etc.

Qualitative Data Analysis

Given that you have invested time and money in gathering your data, analysis of it is essential. You don't want to end up in the dark after making so much effort. Thus, it is a necessary step. There are no predetermined guidelines for assessing this material; the first step is comprehending its two basic methods.

Deductive Strategy

The deductive method entails examining qualitative data following a specified framework. The questions might serve as a roadmap for researchers as they analyze the data. When a researcher has a good sense of the expected replies he or she will obtain from the sample population, they can utilize this quick and simple method.

Inductive Method

Contrarily, the inductive method does not rely on preconceived guidelines or a predefined framework. It is a more extensive and time-consuming method of qualitative data analysis. Researchers frequently employ an inductive technique when they have little or no knowledge about the investigated phenomena.

Key Features Of Qualitative Research

  • Content evaluation. Verbal or behavioral data must be categorized to classify, summarize, and tabulate.
  • Analyzing narratives Utilizing the context of each case and the varied experiences of each respondent, this strategy entails reformulating the narrative that respondents have provided. In other words, narrative analysis is the researcher's reinterpretation of the original qualitative data.
  • Analysis of discourse. A technique for analyzing all kinds of written material, including naturally occurring speech.
  • Framework examination. This more sophisticated approach includes a number of steps, including familiarization, choosing a thematic framework, coding, charting, mapping, and interpretation.
  • Solid theory. This approach to analyzing qualitative data begins with developing a hypothesis by examining a single example. 

Limitations of Qualitative Research

  • The individual talents of the researcher are a major determinant of the research's quality, and the researcher's biases and quirks might have a greater impact.
  • Rigor is more challenging to uphold, gauge, and prove.
  • Analysis and interpretation take a lot of time because of the volume of data.
  • Within the scientific community, it is occasionally not as well understood and accepted as quantitative research.
  • The respondents' replies may be impacted by the researcher's presence, which is frequently unavoidable in qualitative research.
  • Problems with confidentiality and anonymity might arise when disclosing findings
  • Visually describing findings might be more time-consuming and complex.

Advantages Of Qualitative Research

1. understand the attitudes.

Consumer behavior is frequently malleable. Businesses may be left wondering what will happen to them if something happens unexpectedly. Qualitative research methods offer a plausible explanation for why a person's attitude could change.

2. It Generates Content

Even for a seasoned marketer, developing new methods to convey outdated material may be challenging. The qualitative research methodology enables the collection of real thoughts from certain socioeconomic demographics.

3. It Reduces Costs

Comparatively speaking to other research techniques, qualitative research employs a smaller sample size. This is a result of the fact that each participant is asked for more data. Less expensive research is associated with smaller sample sizes. This method of study not only saves money but it also has the potential to yield quicker findings. This is one of the greatest research methods now accessible if data is required rapidly for a crucial decision.

4. Offer Insights Unique To A Certain Sector

The two key elements for retaining customers are relationships and engagement. To communicate with their core demographics in a way that is as accurate and authentic as possible, modern organizations may employ qualitative research to uncover fresh insights that help advance these two essential elements.

5. Enables Creativity To Act As A Catalyst

Facts are frequently preferred above views in research. Instead of innovation, it wants observations. Unlike standard research, qualitative research follows a distinct path. Using this format, respondents won't seek to answer questions in a way that would suit the researcher, which tends to introduce bias into the collected data.

6. Ongoing, Open-Ended Process

Many people have a conditioned, skimpy response that they develop out of habit. Researchers can go further into these behaviors to uncover the real facts that a subject might offer by using the qualitative research technique. It has access to the emotional information that influences how we make decisions.

7. Takes Into Account Human Experience

Facts are crucial. Statistics can reveal patterns. The human experience, however, cannot be disregarded. Two people will each perceive the identical incident differently due to their unique human experiences. The intricacy of this material may be included in the findings drawn from the gathered study by conducting qualitative research.

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What is Quantitative Research?

The process of gathering and interpreting numerical data is known as quantitative research. In addition to identifying trends and averaging data, hypotheses can be formulated, causality can be examined, and findings can be extrapolated to greater populations. A comparative study, which gathers and examines non-numerical data, is known as quantitative research (e.g., text, video, or audio). The scientific and social sciences, including biology, chemistry, psychology, economics, sociology, and marketing, frequently employ quantitative research.

Key Features of Quantitative Research

The goal of descriptive research is to describe the current situation of a chosen variable. The purpose of these studies is to offer systematic data regarding phenomena. The researcher typically does not start with a hypothesis but is more likely to do so after gathering evidence. The hypothesis is tested through the analysis and synthesis of the data.

Using statistical data, correlational research aims to quantify the strength of a link between two or more variables. Relationships between and among various facts are looked for and understood in this design style. While this kind of study will spot trends and patterns in data, it does not go as far as to show the reasons behind the observed patterns.

The goal of causal-comparative/quasi-experimental research is to identify the causal links between the variables. Although there are some significant variations, these designs are extremely comparable to actual studies. The effects of an independent variable on the dependent variable are measured, but the investigator does not change the independent variable. The researcher must take advantage of naturally occurring or pre-existing groupings rather than create them randomly.

The scientific method, also known as real experimentation, is used in experimental research to determine the cause-and-effect link between the many study-related factors. The actual experiment is frequently viewed as a laboratory study, although this is not necessarily the case; the lab environment has no bearing on it.

Limitations of Quantitative Research

The fact that quantitative research techniques only provide a surface-level understanding of a phenomenon and ignore test-takers and testers' experiences as well as what they mean by certain terms is one of its limitations.

Advantages of Quantitative Research

1. can be examined and tested.

To do quantitative research, thorough experimental planning and the capacity for universal test and result replication are essential. As a result, the information you collect is more trustworthy and less subject to debate.

2. Clearly Stated Analysis 

The findings you get from collecting quantitative data can help you decide which statistical tests to run. As a result, your data interpretation and presentation of your findings will be simple and less vulnerable to mistakes and subjectivity.

3. Prestige

Many individuals don't comprehend the mathematics needed in such research; thus, it is valued and remarkable when it requires extensive statistics and data analysis. Technical innovations like computer modeling, stock picking, portfolio evaluation, and other data-driven business choices are connected to quantitative research.

Qualitative vs Quantitative Research - A Comparison

Learn the latest trends in data analytics.

Learn The Latest Trends in Data Analytics!

When Do You Use Qualitative and Quantitative Research?

To understand qualitative research, let’s take the following example.

Qualitative Research Examples


Suppose a bookstore owner is looking for ways to improve their sales and customer outreach. An online community of readers who were the bookstore's loyal customers were interviewed, and related questions were asked, and they answered the questions. In the end, it was found that most of the books in the stores were for adults, and there were not sufficient books for children or teenagers.

By conducting this qualitative research, the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research, the bookstore owner can now keep books for different age groups and improve his sales and customer outreach. 

Qualitative Research Examples


Let's consider another example to understand quantitative research. Suppose any organization likes to conduct a customer satisfaction (CSAT) survey. For that, a customer satisfaction survey template can be implemented. Through this survey, a company can collect quantitative data and metrics on the goodwill of the brand or the company in the mind of the customer based on several parameters such as product quality, pricing, and customer experience. This data can be gathered by asking a net promoter score (NPS) question, and matrix table questions that provide data in the form of numbers that can be analyzed and worked upon using various analytics tools.

Now, let’s talk about Qualitative vs. Quantitative Research based on how data is collected for these research methods. 

Data Collection

Now, let’s talk about Qualitative vs. Quantitative Research based on the kind of research approaches they adopt.

Research Approach

For any research, sample data is important to derive meaningful information. Let’s understand Qualitative vs. Quantitative Research based on research samples.

Research Samples

With that, let’s now get an idea about the role of the researcher in qualitative and quantitative research.

Role of the Researcher

Now, let’s learn about Qualitative vs. Quantitative Research based on the scientific methods that are used in these techniques.

Scientific Method

Analyzing data, final report.

You may prefer to use only one type of research within a study, but the data generated from the research might not provide the desired results. To implement an unbiased research project that will provide accurate and meaningful insights, it is advised to consider both qualitative and quantitative research methods to get the right results. After reading this article, you would have learned the major differences between qualitative and quantitative research. 

If you want to learn more about different research techniques or how they impact your data and data analysis, then check out our extensive course on Data Analytics . Get in-depth with your analysis and jumpstart your career as a Data Analyst.

Do you have any questions related to Qualitative vs Quantitative Research? If so, then please put it in the comments section of this article. Our team will help you solve your queries at the earliest. 

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About the author.

Avijeet Biswal

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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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.

qualitative and quantitative data analysis in research

  • 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.


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.

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Qualitative and Quantitative Data Analysis Methods

Written by Ruchir Dahal on October 18, 2021

Qualitative and Quantitative Data Analysis Methods

This is one piece of a three-part series that looks at the various methods, techniques , and essential steps to ensure superior data analysis.

The majority of leaders from high-performing businesses attribute their success to data analytics. According to a survey done by McKinsey & Company , respondents from these companies are three times more likely to pin their accomplishments on data analytics. 

That being said, although 1.145 trillion MB of data is created every day, stats show only 0.5% of it is analyzed to get results. This highlights a huge gap between companies that use data analytics to get ahead and those that don’t.

Grepsr has helped businesses bridge this gap, which in turn has given them an upper hand over their competitors. If you haven’t swiveled to data analytics yet, there is no better time to get started.

content extraction service

In the last post , we talked about the ‘why’ of data analysis, this time we will delve into the ‘how’. 

Qualitative and quantitative data

The internet has enabled us to create large volumes of data at a staggering pace. Moreover, the way you analyze it depends on the type of data you are working with. We broadly classify data into two forms — qualitative and quantitative .

Qualitative and Quantitative Data

Methods to data analysis

With so much data being created every day, it becomes imperative to go beyond the traditional methods to analyze this huge chunk of invaluable information.  

Qualitative and quantitative data each have their own ways of being processed. 

  • Qualitative data analysis comes into play when the data you are trying to process has no bearing whatsoever with numbers, and cannot be tabulated — e.g. text, videos, images, etc.
  • Quantitative data analysis deals with structured datasets that have numbers in them. It runs algorithms on statistical data to deduce objective truths.

Learn the key differences between qualitative and quantitative research from our recent blog.

qualitative and quantitative data analysis in research

Quantitative data analysis

Quantitative data analysis is a more traditional form of analysis. As mentioned earlier, this process crunches numbers to get results.  

Since one of the major functions of this process is to run algorithms on statistical data to obtain the outcome, the methods used in quantitative data analytics range from basic calculations like mean, median, and mode to more advanced deductions such as correlations and regressions.

Some of the scopes of quantitative data analysis include: 

  • Project management
  • Research and Development
  • Product planning

Qualitative data analysis

Qualitative data analysis is used when the data you are trying to process cannot be adjusted in rows and columns. It involves the identification, examination, and elucidation of themes and patterns in data (mostly textual) to bolster the decision-making process.

Unlike quantitative analysis, qualitative data analysis is subjective . This method of analysis allows us to move beyond the quantitative traits of data and explore new avenues to make informed decisions.

The following are some of the scopes of qualitative data analysis: 

  • Measuring customer satisfaction
  • Monitoring competition
  • Analyzing customer behavior
  • Evaluating market trends

Learn more about qualitative data analysis in detail:

qualitative and quantitative data analysis in research

Differences between qualitative and quantitative data analysis

Qualitative and Quantitative Data

A veritable combination: qualitative and quantitative data analysis

Quantitative and qualitative data analysis when used together can help you generate deeper insights . More often than not, quantitative and qualitative data can be collected from the same data unit as you can see below.

Qualitative and Quantitative Data

To gain richer insights you can even pair these two methods in different domains. Best use cases include Google analytics & user interviews, social media & community engagement, marketing & surveys, and so on.

Bear in mind that your way of analysis completely depends on your requirements. Sometimes, quantitative analysis will be more than enough, and other times, only qualitative will do just fine. When you want to dig deep into the data at hand, it is advisable that you go with both qualitative and quantitative data analysis.

If you want to learn more about the different techniques to perform qualitative and quantitative data analysis, click here . To add to that, if you ever need to analyze large amounts of data or need expert help, you can hire the top freelance data analysts to augment your development teams for data analysis.

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Quantitative research: Understanding the approaches and key elements

Quantitative Research Understanding The Approaches And Key Elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

  • Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
  • Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
  • Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

  • Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
  • Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
  • Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
  • Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
  • Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
  • Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

Canvs AI: Unlock critical insights from unstructured feedback Related Categories: Research Industry, Data Analysis, Quantitative Research Research Industry, Data Analysis, Quantitative Research, Artificial Intelligence / AI, Text Analytics

Segmentation in the pharma industry: How to create resilient strategies Related Categories: Research Industry, Sampling, Survey Research Research Industry, Sampling, Survey Research, Market Segmentation Studies, Segmentation Studies, Health Care (Healthcare), Health Care (Healthcare) Research, Patients , Questionnaire Analysis, Social Media Research

Leveraging AI to unlock qualitative research at scale Related Categories: Research Industry, Quantitative Research, Sampling, Hybrid Research (Qual/Quant) Research Industry, Quantitative Research, Sampling, Hybrid Research (Qual/Quant), Artificial Intelligence / AI, Qualitative Research, Attitude/Usage Studies, Consumer Research, Consumers, Data Visualization/Infographics

Situational choice experiments for marketing research Related Categories: Research Industry, Data Analysis, Survey Research Research Industry, Data Analysis, Survey Research, Conjoint Analysis/Trade-Off Analysis, Discrete Choice Modeling, Physicians, Software-Conjoint Analysis, Questionnaire Analysis

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What is Qualitative Data Analysis?

Understanding qualitative information analysis is important for researchers searching for to uncover nuanced insights from non-numerical statistics. By exploring qualitative statistics evaluation, you can still draw close its importance in studies, understand its methodologies, and determine while and the way to apply it successfully to extract meaningful insights from qualitative records.

The article goals to provide a complete manual to expertise qualitative records evaluation, masking its significance, methodologies, steps, advantages, disadvantages, and applications.


Table of Content

Understanding Qualitative Data Analysis

Importance of qualitative data analysis, steps to perform qualitative data analysis, 1. craft clear research questions, 2. gather rich customer insights, 3. organize and categorize data, 4. uncover themes and patterns : coding, 5. make hypotheses and validating, methodologies in qualitative data analysis, advantages of qualitative data analysis, disadvantages of qualitative data analysis, when qualitative data analysis is used, applications of qualitative data analysis.

Qualitative data analysis is the process of systematically examining and deciphering qualitative facts (such as textual content, pix, motion pictures, or observations) to discover patterns, themes, and meanings inside the statistics· Unlike quantitative statistics evaluation, which focuses on numerical measurements and statistical strategies, qualitative statistics analysis emphasizes know-how the context, nuances, and subjective views embedded inside the information.

Qualitative facts evaluation is crucial because it is going past the bloodless hard information and numbers to provide a richer expertise of why and the way things appear. Qualitative statistics analysis is important for numerous motives:

  • Understanding Complexity and unveils the “Why” : Quantitative facts tells you “what” came about (e· g·, sales figures), however qualitative evaluation sheds light on the motives in the back of it (e·g·, consumer comments on product features).
  • Contextual Insight : Numbers don’t exist in a vacuum. Qualitative information affords context to quantitative findings, making the bigger photo clearer· Imagine high customer churn – interviews would possibly monitor lacking functionalities or perplexing interfaces.
  • Uncovers Emotions and Opinions: Qualitative records faucets into the human element· Surveys with open ended questions or awareness companies can display emotions, critiques, and motivations that can’t be captured by using numbers on my own.
  • Informs Better Decisions: By understanding the “why” and the “how” at the back of customer behavior or employee sentiment, companies can make greater knowledgeable decisions about product improvement, advertising techniques, and internal techniques.
  • Generates New Ideas : Qualitative analysis can spark clean thoughts and hypotheses· For example, via analyzing consumer interviews, commonplace subject matters may emerge that cause totally new product features.
  • Complements Quantitative Data : While each facts sorts are precious, they paintings quality collectively· Imagine combining website site visitors records (quantitative) with person comments (qualitative) to apprehend user revel in on a particular webpage.

In essence, qualitative data evaluation bridges the gap among the what and the why, providing a nuanced know-how that empowers better choice making·


Qualitative data analysis process, follow the structure in below steps:

Qualitative information evaluation procedure, comply with the shape in underneath steps:

Before diving into evaluation, it is critical to outline clear and particular studies questions. These questions ought to articulate what you want to study from the data and manual your analysis towards actionable insights. For instance, asking “How do employees understand the organizational culture inside our agency?” helps makes a speciality of know-how personnel’ perceptions of the organizational subculture inside a selected business enterprise. By exploring employees’ perspectives, attitudes, and stories related to organizational tradition, researchers can find valuable insights into workplace dynamics, communication patterns, management patterns, and worker delight degrees.

There are numerous methods to acquire qualitative information, each offering specific insights into client perceptions and reviews.

  • User Feedback: In-app surveys, app rankings, and social media feedback provide direct remarks from users approximately their studies with the products or services.
  • In-Depth Interviews : One-on-one interviews allow for deeper exploration of particular topics and offer wealthy, special insights into individuals’ views and behaviors.
  • Focus Groups : Facilitating group discussions allows the exploration of numerous viewpoints and permits individuals to construct upon every different’s ideas.
  • Review Sites : Analyzing purchaser critiques on systems like Amazon, Yelp, or app shops can monitor not unusual pain points, pride levels, and areas for improvement.
  • NPS Follow-Up Questions : Following up on Net Promoter Score (NPS) surveys with open-ended questions allows customers to elaborate on their rankings and provides qualitative context to quantitative ratings.

Efficient facts below is crucial for powerful analysis and interpretation.

  • Centralize: Gather all qualitative statistics, along with recordings, notes, and transcripts, right into a valuable repository for smooth get admission to and control.
  • Categorize through Research Question : Group facts primarily based at the specific studies questions they deal with. This organizational structure allows maintain consciousness in the course of analysis and guarantees that insights are aligned with the research objectives.

Coding is a scientific manner of assigning labels or categories to segments of qualitative statistics to uncover underlying issues and patterns.

  • Theme Identification : Themes are overarching principles or ideas that emerge from the records· During coding, researchers perceive and label segments of statistics that relate to those themes, bearing in mind the identification of vital principles in the dataset.
  • Pattern Detection : Patterns seek advice from relationships or connections between exceptional elements in the statistics. By reading coded segments, researchers can locate trends, repetitions, or cause-and-effect relationships, imparting deeper insights into patron perceptions and behaviors.

Based on the identified topics and styles, researchers can formulate hypotheses and draw conclusions about patron experiences and choices.

  • Hypothesis Formulation: Hypotheses are tentative causes or predictions based on found styles within the information. Researchers formulate hypotheses to provide an explanation for why certain themes or styles emerge and make predictions approximately their effect on patron behavior.
  • Validation : Researchers validate hypotheses by means of segmenting the facts based on one-of-a-kind standards (e.g., demographic elements, usage patterns) and analyzing variations or relationships inside the records. This procedure enables enhance the validity of findings and offers proof to assist conclusions drawn from qualitative evaluation.

There are five common methodologies utilized in Qualitative Data Analysis·

  • Thematic Analysis : Thematic Analysis involves systematically figuring out and reading habitual subject matters or styles within qualitative statistics. Researchers begin with the aid of coding the facts, breaking it down into significant segments, and then categorizing these segments based on shared traits. Through iterative analysis, themes are advanced and refined, permitting researchers to benefit insight into the underlying phenomena being studied.
  • Content Analysis: Content Analysis focuses on reading textual information to pick out and quantify particular styles or issues. Researchers code the statistics primarily based on predefined classes or subject matters, taking into consideration systematic agency and interpretation of the content. By analyzing how frequently positive themes occur and the way they’re represented inside the data, researchers can draw conclusions and insights relevant to their research objectives.
  • Narrative Analysis: Narrative Analysis delves into the narrative or story within qualitative statistics, that specialize in its structure, content, and meaning. Researchers examine the narrative to understand its context and attitude, exploring how individuals assemble and speak their reports thru storytelling. By analyzing the nuances and intricacies of the narrative, researchers can find underlying issues and advantage a deeper know-how of the phenomena being studied.
  • Grounded Theory : Grounded Theory is an iterative technique to growing and checking out theoretical frameworks primarily based on empirical facts. Researchers gather, code, and examine information without preconceived hypotheses, permitting theories to emerge from the information itself. Through constant assessment and theoretical sampling, researchers validate and refine theories, main to a deeper knowledge of the phenomenon under investigation.
  • Phenomenological Analysis : Phenomenological Analysis objectives to discover and recognize the lived stories and views of people. Researchers analyze and interpret the meanings, essences, and systems of these reviews, figuring out not unusual topics and styles across individual debts. By immersing themselves in members’ subjective stories, researchers advantage perception into the underlying phenomena from the individuals’ perspectives, enriching our expertise of human behavior and phenomena.
  • Richness and Depth: Qualitative records evaluation lets in researchers to discover complex phenomena intensive, shooting the richness and complexity of human stories, behaviors, and social processes.
  • Flexibility : Qualitative techniques offer flexibility in statistics collection and evaluation, allowing researchers to conform their method based on emergent topics and evolving studies questions.
  • Contextual Understanding: Qualitative evaluation presents perception into the context and meaning of information, helping researchers recognize the social, cultural, and historic elements that form human conduct and interactions.
  • Subjective Perspectives : Qualitative methods allow researchers to explore subjective perspectives, beliefs, and reviews, offering a nuanced know-how of people’ mind, emotions, and motivations.
  • Theory Generation : Qualitative information analysis can cause the generation of recent theories or hypotheses, as researchers uncover patterns, themes, and relationships in the records that might not were formerly recognized.
  • Subjectivity: Qualitative records evaluation is inherently subjective, as interpretations can be stimulated with the aid of researchers’ biases, views, and preconceptions .
  • Time-Intensive : Qualitative records analysis may be time-consuming, requiring giant data collection, transcription, coding, and interpretation.
  • Generalizability: Findings from qualitative studies might not be effortlessly generalizable to larger populations, as the focus is often on know-how unique contexts and reviews in preference to making statistical inferences.
  • Validity and Reliability : Ensuring the validity and reliability of qualitative findings may be difficult, as there are fewer standardized methods for assessing and establishing rigor in comparison to quantitative studies.
  • Data Management : Managing and organizing qualitative information, together with transcripts, subject notes, and multimedia recordings, can be complicated and require careful documentation and garage.
  • Exploratory Research: Qualitative records evaluation is nicely-suited for exploratory studies, wherein the aim is to generate hypotheses, theories, or insights into complex phenomena.
  • Understanding Context : Qualitative techniques are precious for knowledge the context and which means of statistics, in particular in studies wherein social, cultural, or ancient factors are vital.
  • Subjective Experiences : Qualitative evaluation is good for exploring subjective stories, beliefs, and views, providing a deeper knowledge of people’ mind, feelings, and behaviors.
  • Complex Phenomena: Qualitative strategies are effective for studying complex phenomena that can not be effortlessly quantified or measured, allowing researchers to seize the richness and depth of human stories and interactions.
  • Complementary to Quantitative Data: Qualitative information analysis can complement quantitative research by means of offering context, intensity, and insight into the meanings at the back of numerical statistics, enriching our knowledge of studies findings.
  • Social Sciences: Qualitative information analysis is widely utilized in social sciences to apprehend human conduct, attitudes, and perceptions. Researchers employ qualitative methods to delve into the complexities of social interactions, cultural dynamics, and societal norms. By analyzing qualitative records which include interviews, observations, and textual resources, social scientists benefit insights into the elaborate nuances of human relationships, identity formation, and societal structures.
  • Psychology : In psychology, qualitative data evaluation is instrumental in exploring and deciphering person reports, emotions, and motivations. Qualitative methods along with in-depth interviews, cognizance businesses, and narrative evaluation allow psychologists to delve deep into the subjective stories of individuals. This approach facilitates discover underlying meanings, beliefs, and emotions, dropping light on psychological processes, coping mechanisms, and personal narratives.
  • Anthropology : Anthropologists use qualitative records evaluation to look at cultural practices, ideals, and social interactions inside various groups and societies. Through ethnographic research strategies such as player statement and interviews, anthropologists immerse themselves within the cultural contexts of different agencies. Qualitative analysis permits them to find the symbolic meanings, rituals, and social systems that form cultural identification and behavior.
  • Qualitative Market Research : In the sphere of marketplace research, qualitative statistics analysis is vital for exploring consumer options, perceptions, and behaviors. Qualitative techniques which include consciousness groups, in-depth interviews, and ethnographic research permit marketplace researchers to gain a deeper understanding of customer motivations, choice-making methods, and logo perceptions· By analyzing qualitative facts, entrepreneurs can identify emerging developments, discover unmet wishes, and tell product development and advertising and marketing techniques.
  • Healthcare: Qualitative statistics analysis plays a important function in healthcare studies via investigating patient experiences, delight, and healthcare practices. Researchers use qualitative techniques which includes interviews, observations, and patient narratives to explore the subjective reviews of people inside healthcare settings. Qualitative evaluation helps find affected person perspectives on healthcare services, treatment consequences, and pleasant of care, facilitating enhancements in patient-targeted care delivery and healthcare policy.

Qualitative data evaluation offers intensity, context, and know-how to investigate endeavors, enabling researchers to find wealthy insights and discover complicated phenomena via systematic examination of non-numerical information.

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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.


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 ).

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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 ).

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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).

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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.


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.

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Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights

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  • Published: 11 May 2024

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qualitative and quantitative data analysis in research

  • Sourena Rahmani 1 ,
  • Alireza Goli   ORCID: orcid.org/0000-0001-9535-9902 1 &
  • Ali Zackery 1  

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The global community is actively pursuing alternative energy sources to mitigate environmental concerns and decrease dependence on fossil fuels. Biodiesel, recognized as a clean and eco-friendly fuel with advantages over petroleum-based alternatives, has been identified as a viable substitute. However, its commercialization encounters challenges due to costly production processes. Establishing a more efficient supply chain for mass production and distribution could surmount these obstacles, rendering biodiesel a cost-effective solution. Despite numerous review articles across various renewable energy supply chain domains, there remains a gap in the literature specifically addressing the biodiesel supply chain network design. This research entails a comprehensive systematic literature review (SLR) focusing on the design of biodiesel supply chain networks. The primary objective is to formulate an economically, environmentally, and socially optimized supply chain framework. The review also seeks to offer a holistic overview of pertinent technical terms and key activities involved in these supply chains. Through this SLR, a thorough examination and synthesis of existing literature will yield valuable insights into the design and optimization of biodiesel supply chains. Additionally, it will identify critical research gaps in the field, proposing the exploration of fourth-generation feedstocks, integration of multi-channel chains, and the incorporation of sustainability and resilience aspects into the supply chain network design. These proposed areas aim to address existing knowledge gaps and enhance the overall effectiveness of biodiesel supply chain networks.

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Rahmani, S., Goli, A. & Zackery, A. Biodiesel supply chain network design: a comprehensive review with qualitative and quantitative insights. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33392-w

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    4. Quantitative vs qualitative data: methods of analysis. Another major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes.

  10. Qualitative vs Quantitative Research

    Qualitative vs. quantitative research: Methods & data analysis. It might be easy to get bogged down in a "qualitative vs. quantitative data" debate, particularly when quantitative and qualitative research seem like very different things. However, both qualitative and quantitative data have their uses in research.

  11. Qualitative vs Quantitative Data: Analysis, Definitions, Examples

    Key characteristics of quantitative data: It can be quantified and verified. Data can be counted. Data type: number and statistics. It answers questions such as "how many, "how much" and "how often". Examples of quantitative data: Scores on tests and exams e.g. 85, 67, 90 and etc. The weight of a person or a subject.

  12. Data Analysis Methods: Qualitative vs. Quantitative

    Couchbase Product Marketing on February 13, 2024. Data analysis is a crucial step in extracting meaningful insights from collected data. Two common approaches to analyzing data are qualitative and quantitative analysis. Each method offers different techniques for interpreting and understanding your findings. This blog post will further explore ...

  13. How to use and assess qualitative research methods

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

  14. Qualitative Research: Data Collection, Analysis, and Management

    Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management.

  15. Qualitative vs. Quantitative Data in Research: The Difference

    Qualitative data is subjective and unique. Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing. Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

  16. The Ultimate Guide to Qualitative vs. Quantitative Research

    The time expenditure is lesser on the planning end and more during the analysis phase. Quantitative research aims more on the ability to compute numbers and perform statistical analysis. Quantitative research helps you generate numerical data and hard facts using statistical, logical, and mathematical techniques.

  17. Difference Between Qualitative and Qualitative Research

    At a Glance. Psychologists rely on quantitative and quantitative research to better understand human thought and behavior. Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions. Quantitative research involves collecting and evaluating numerical data.

  18. Data Analysis in Quantitative Research

    Abstract. Quantitative data analysis serves as part of an essential process of evidence-making in health and social sciences. It is adopted for any types of research question and design whether it is descriptive, explanatory, or causal. However, compared with qualitative counterpart, quantitative data analysis has less flexibility.

  19. (PDF) Qualitative Data Analysis and Interpretation: Systematic Search

    The approaches to qualitative and quantitative data analysis are different, as illustrated in table 8.1 below. The remarkable growth of qualitative research in many disciplines, including business ...

  20. Qualitative and Quantitative Data Analysis Methods

    Product planning. Qualitative data analysis. Qualitative data analysis is used when the data you are trying to process cannot be adjusted in rows and columns. It involves the identification, examination, and elucidation of themes and patterns in data (mostly textual) to bolster the decision-making process.

  21. Qualitative Study

    Qualitative research is a type of research that explores and provides deeper insights into real-world problems.[1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences ...

  22. Quantitative research: Understanding the approaches and key elements

    What is quantitative research? The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell.

  23. Combining Qualitative and Quantitative Research

    Today, researchers and organisations are embracing holistic solutions that offer comprehensive analytics capabilities and incorporate both qualitative and quantitative data. By combining these research methods, organisations can gain deeper insights, evaluate hypotheses, compare results, and seamlessly integrate insights into every phase of the ...

  24. What is Qualitative Data Analysis?

    Understanding Qualitative Data Analysis. Qualitative data analysis is the process of systematically examining and deciphering qualitative facts (such as textual content, pix, motion pictures, or observations) to discover patterns, themes, and meanings inside the statistics· Unlike quantitative statistics evaluation, which focuses on numerical measurements and statistical strategies ...

  25. Structured vs. unstructured data: What's the difference?

    Unstructured data, typically categorized as qualitative data, cannot be processed and analyzed through conventional data tools and methods. Since unstructured data does not have a predefined data model, it is best managed in non-relational (NoSQL) databases.Another way to manage unstructured data is to use data lakes to preserve it in raw form. ...

  26. Quality markers of Polygala fallax Hemsl decoction based on qualitative

    Drug Development Research; Drug Testing and Analysis; Electroanalysis; Electrophoresis; Engineering in Life Sciences ... Quality markers of Polygala fallax Hemsl decoction based on qualitative and quantitative analysis combined with network pharmacology and chemometric ... The data that support the findings of this study are available from the ...

  27. Quantitative and Qualitative Approaches to Generalization and

    3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick nor the Oxford Handbook of Qualitative Research edited by Leavy 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 ...

  28. Biodiesel supply chain network design: a comprehensive ...

    This paper comprehensively reviews qualitative and quantitative analyses, elucidating key aspects and challenges in biodiesel supply chain design and optimization. Highlighting biodiesel's significance as a sustainable alternative, it explores diverse methodologies, models, and tools for supply chain analysis.