why does qualitative research requires few respondents

How many participants do I need for qualitative research?

• Participant recruitment
• Qualitative research

6 min read David Renwick

For those new to the qualitative research space, there’s one question that’s usually pretty tough to figure out, and that’s the question of how many participants to include in a study. Regardless of whether it’s research as part of the discovery phase for a new product, or perhaps an in-depth canvas of the users of an existing service, researchers can often find it difficult to agree on the numbers. So is there an easy answer? Let’s find out.

Here, we’ll look into the right number of participants for qualitative research studies. If you want to know about participants for quantitative research, read Nielsen Norman Group’s article .

Getting the numbers right

So you need to run a series of user interviews or usability tests and aren’t sure exactly how many people you should reach out to. It can be a tricky situation – especially for those without much experience. Do you test a small selection of 1 or 2 people to make the recruitment process easier? Or, do you go big and test with a series of 10 people over the course of a month? The answer lies somewhere in between.

It’s often a good idea (for qualitative research methods like interviews and usability tests) to start with 5 participants and then scale up by a further 5 based on how complicated the subject matter is. You may also find it helpful to add additional participants if you’re new to user research or you’re working in a new area.

What you’re actually looking for here is what’s known as saturation.

Understanding saturation

Whether it’s qualitative research as part of a master’s thesis or as research for a new online dating app, saturation is the best metric you can use to identify when you’ve hit the right number of participants.

In a nutshell, saturation is when you’ve reached the point where adding further participants doesn’t give you any further insights. It’s true that you may still pick up on the occasional interesting detail, but all of your big revelations and learnings have come and gone. A good measure is to sit down after each session with a participant and analyze the number of new insights you’ve noted down.

Interestingly, in a paper titled How Many Interviews Are Enough? , authors Greg Guest, Arwen Bunce and Laura Johnson noted that saturation usually occurs with around 12 participants in homogeneous groups (meaning people in the same role at an organization, for example). However, carrying out ethnographic research on a larger domain with a diverse set of participants will almost certainly require a larger sample.

Ensuring you’ve hit the right number of participants

How do you know when you’ve reached saturation point? You have to keep conducting interviews or usability tests until you’re no longer uncovering new insights or concepts.

While this may seem to run counter to the idea of just gathering as much data from as many people as possible, there’s a strong case for focusing on a smaller group of participants. In The logic of small samples in interview-based , authors Mira Crouch and Heather McKenzie note that using fewer than 20 participants during a qualitative research study will result in better data. Why? With a smaller group, it’s easier for you (the researcher) to build strong close relationships with your participants, which in turn leads to more natural conversations and better data.

There’s also a school of thought that you should interview 5 or so people per persona. For example, if you’re working in a company that has well-defined personas, you might want to use those as a basis for your study, and then you would interview 5 people based on each persona. This maybe worth considering or particularly important when you have a product that has very distinct user groups (e.g. students and staff, teachers and parents etc).

How your domain affects sample size

The scope of the topic you’re researching will change the amount of information you’ll need to gather before you’ve hit the saturation point. Your topic is also commonly referred to as the domain.

If you’re working in quite a confined domain, for example, a single screen of a mobile app or a very specific scenario, you’ll likely find interviews with 5 participants to be perfectly fine. Moving into more complicated domains, like the entire checkout process for an online shopping app, will push up your sample size.

As Mitchel Seaman notes : “Exploring a big issue like young peoples’ opinions about healthcare coverage, a broad emotional issue like postmarital sexuality, or a poorly-understood domain for your team like mobile device use in another country can drastically increase the number of interviews you’ll want to conduct.”

In-person or remote

Does the location of your participants change the number you need for qualitative user research? Well, not really – but there are other factors to consider.

• Budget: If you choose to conduct remote interviews/usability tests, you’ll likely find you’ve got lower costs as you won’t need to travel to your participants or have them travel to you. This also affects…
• Participant access: Remote qualitative research can be a lifesaver when it comes to participant access. No longer are you confined to the people you have physical access to — instead you can reach out to anyone you’d like.
• Quality: On the other hand, remote research does have its downsides. For one, you’ll likely find you’re not able to build the same kinds of relationships over the internet or phone as those in person, which in turn means you never quite get the same level of insights.

Is there value in outsourcing recruitment?

Recruitment is understandably an intensive logistical exercise with many moving parts. If you’ve ever had to recruit people for a study before, you’ll understand the need for long lead times (to ensure you have enough participants for the project) and the countless long email chains as you discuss suitable times.

Outsourcing your participant recruitment is just one way to lighten the logistical load during your research. Instead of having to go out and look for participants, you have them essentially delivered to you in the right number and with the right attributes.

We’ve got one such service at Optimal Workshop, which means it’s the perfect accompaniment if you’re also using our platform of UX tools. Read more about that here .

So that’s really most of what there is to know about participant recruitment in a qualitative research context. As we said at the start, while it can appear quite tricky to figure out exactly how many people you need to recruit, it’s actually not all that difficult in reality.

Overall, the number of participants you need for your qualitative research can depend on your project among other factors. It’s important to keep saturation in mind, as well as the locale of participants. You also need to get the most you can out of what’s available to you. Remember: Some research is better than none!

Capture, analyze and visualize your qualitative data.

Try our qualitative research tool for usability testing, interviewing and note-taking. Reframer by Optimal Workshop.

Published on August 8, 2019

David Renwick

David is Optimal Workshop's Content Strategist and Editor of CRUX. You can usually find him alongside one of the office dogs 🐕 (Bella, Bowie, Frida, Tana or Steezy). Connect with him on LinkedIn.

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Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. 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, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Copyright © 2024, StatPearls Publishing LLC.

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• Study Guide

Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

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

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

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

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

Qualitative vs Quantitative: Overview  

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

Qualitative Research 101: The Basics

The bathwater is hot.

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

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

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

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

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

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

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

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

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

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

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

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

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

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

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

Need a helping hand?

Qualitative vs Quantitative Research: Which one should you use?

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

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

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

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

Let’s look at an example.

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

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

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

Qualitative vs Quantitative Research: Data collection and analysis

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

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

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

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

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

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

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

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

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

Let’s start with qualitative “pros”

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

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

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

Qualitative research also has its “cons”:

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

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

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

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

Now let’s turn to quantitative “pros”:

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

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

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

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

But there are also quantitative “cons”:

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

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

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

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

Mixed methods: a happy medium?

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

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

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

Recap: Qualitative vs Quantitative Research

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

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

Psst... there’s more!

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

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

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Organizing Your Social Sciences Research Paper

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The word qualitative implies an emphasis on the qualities of entities and on processes and meanings that are not experimentally examined or measured [if measured at all] in terms of quantity, amount, intensity, or frequency. Qualitative researchers stress the socially constructed nature of reality, the intimate relationship between the researcher and what is studied, and the situational constraints that shape inquiry. Such researchers emphasize the value-laden nature of inquiry. They seek answers to questions that stress how social experience is created and given meaning. In contrast, quantitative studies emphasize the measurement and analysis of causal relationships between variables, not processes. Qualitative forms of inquiry are considered by many social and behavioral scientists to be as much a perspective on how to approach investigating a research problem as it is a method.

Denzin, Norman. K. and Yvonna S. Lincoln. “Introduction: The Discipline and Practice of Qualitative Research.” In The Sage Handbook of Qualitative Research . Norman. K. Denzin and Yvonna S. Lincoln, eds. 3 rd edition. (Thousand Oaks, CA: Sage, 2005), p. 10.

Characteristics of Qualitative Research

Below are the three key elements that define a qualitative research study and the applied forms each take in the investigation of a research problem.

• Naturalistic -- refers to studying real-world situations as they unfold naturally; non-manipulative and non-controlling; the researcher is open to whatever emerges [i.e., there is a lack of predetermined constraints on findings].
• Emergent -- acceptance of adapting inquiry as understanding deepens and/or situations change; the researcher avoids rigid designs that eliminate responding to opportunities to pursue new paths of discovery as they emerge.
• Purposeful -- cases for study [e.g., people, organizations, communities, cultures, events, critical incidences] are selected because they are “information rich” and illuminative. That is, they offer useful manifestations of the phenomenon of interest; sampling is aimed at insight about the phenomenon, not empirical generalization derived from a sample and applied to a population.

The Collection of Data

• Data -- observations yield a detailed, "thick description" [in-depth understanding]; interviews capture direct quotations about people’s personal perspectives and lived experiences; often derived from carefully conducted case studies and review of material culture.
• Personal experience and engagement -- researcher has direct contact with and gets close to the people, situation, and phenomenon under investigation; the researcher’s personal experiences and insights are an important part of the inquiry and critical to understanding the phenomenon.
• Empathic neutrality -- an empathic stance in working with study respondents seeks vicarious understanding without judgment [neutrality] by showing openness, sensitivity, respect, awareness, and responsiveness; in observation, it means being fully present [mindfulness].
• Dynamic systems -- there is attention to process; assumes change is ongoing, whether the focus is on an individual, an organization, a community, or an entire culture, therefore, the researcher is mindful of and attentive to system and situational dynamics.

The Analysis

• Unique case orientation -- assumes that each case is special and unique; the first level of analysis is being true to, respecting, and capturing the details of the individual cases being studied; cross-case analysis follows from and depends upon the quality of individual case studies.
• Inductive analysis -- immersion in the details and specifics of the data to discover important patterns, themes, and inter-relationships; begins by exploring, then confirming findings, guided by analytical principles rather than rules.
• Holistic perspective -- the whole phenomenon under study is understood as a complex system that is more than the sum of its parts; the focus is on complex interdependencies and system dynamics that cannot be reduced in any meaningful way to linear, cause and effect relationships and/or a few discrete variables.
• Context sensitive -- places findings in a social, historical, and temporal context; researcher is careful about [even dubious of] the possibility or meaningfulness of generalizations across time and space; emphasizes careful comparative case study analysis and extrapolating patterns for possible transferability and adaptation in new settings.
• Voice, perspective, and reflexivity -- the qualitative methodologist owns and is reflective about her or his own voice and perspective; a credible voice conveys authenticity and trustworthiness; complete objectivity being impossible and pure subjectivity undermining credibility, the researcher's focus reflects a balance between understanding and depicting the world authentically in all its complexity and of being self-analytical, politically aware, and reflexive in consciousness.

Berg, Bruce Lawrence. Qualitative Research Methods for the Social Sciences . 8th edition. Boston, MA: Allyn and Bacon, 2012; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 2nd ed. Thousand Oaks, CA: Sage Publications, 1995; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Basic Research Design for Qualitative Studies

Unlike positivist or experimental research that utilizes a linear and one-directional sequence of design steps, there is considerable variation in how a qualitative research study is organized. In general, qualitative researchers attempt to describe and interpret human behavior based primarily on the words of selected individuals [a.k.a., “informants” or “respondents”] and/or through the interpretation of their material culture or occupied space. There is a reflexive process underpinning every stage of a qualitative study to ensure that researcher biases, presuppositions, and interpretations are clearly evident, thus ensuring that the reader is better able to interpret the overall validity of the research. According to Maxwell (2009), there are five, not necessarily ordered or sequential, components in qualitative research designs. How they are presented depends upon the research philosophy and theoretical framework of the study, the methods chosen, and the general assumptions underpinning the study. Goals Describe the central research problem being addressed but avoid describing any anticipated outcomes. Questions to ask yourself are: Why is your study worth doing? What issues do you want to clarify, and what practices and policies do you want it to influence? Why do you want to conduct this study, and why should the reader care about the results? Conceptual Framework Questions to ask yourself are: What do you think is going on with the issues, settings, or people you plan to study? What theories, beliefs, and prior research findings will guide or inform your research, and what literature, preliminary studies, and personal experiences will you draw upon for understanding the people or issues you are studying? Note to not only report the results of other studies in your review of the literature, but note the methods used as well. If appropriate, describe why earlier studies using quantitative methods were inadequate in addressing the research problem. Research Questions Usually there is a research problem that frames your qualitative study and that influences your decision about what methods to use, but qualitative designs generally lack an accompanying hypothesis or set of assumptions because the findings are emergent and unpredictable. In this context, more specific research questions are generally the result of an interactive design process rather than the starting point for that process. Questions to ask yourself are: What do you specifically want to learn or understand by conducting this study? What do you not know about the things you are studying that you want to learn? What questions will your research attempt to answer, and how are these questions related to one another? Methods Structured approaches to applying a method or methods to your study help to ensure that there is comparability of data across sources and researchers and, thus, they can be useful in answering questions that deal with differences between phenomena and the explanation for these differences [variance questions]. An unstructured approach allows the researcher to focus on the particular phenomena studied. This facilitates an understanding of the processes that led to specific outcomes, trading generalizability and comparability for internal validity and contextual and evaluative understanding. Questions to ask yourself are: What will you actually do in conducting this study? What approaches and techniques will you use to collect and analyze your data, and how do these constitute an integrated strategy? Validity In contrast to quantitative studies where the goal is to design, in advance, “controls” such as formal comparisons, sampling strategies, or statistical manipulations to address anticipated and unanticipated threats to validity, qualitative researchers must attempt to rule out most threats to validity after the research has begun by relying on evidence collected during the research process itself in order to effectively argue that any alternative explanations for a phenomenon are implausible. Questions to ask yourself are: How might your results and conclusions be wrong? What are the plausible alternative interpretations and validity threats to these, and how will you deal with these? How can the data that you have, or that you could potentially collect, support or challenge your ideas about what’s going on? Why should we believe your results? Conclusion Although Maxwell does not mention a conclusion as one of the components of a qualitative research design, you should formally conclude your study. Briefly reiterate the goals of your study and the ways in which your research addressed them. Discuss the benefits of your study and how stakeholders can use your results. Also, note the limitations of your study and, if appropriate, place them in the context of areas in need of further research.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Heath, A. W. The Proposal in Qualitative Research. The Qualitative Report 3 (March 1997); Marshall, Catherine and Gretchen B. Rossman. Designing Qualitative Research . 3rd edition. Thousand Oaks, CA: Sage, 1999; Maxwell, Joseph A. "Designing a Qualitative Study." In The SAGE Handbook of Applied Social Research Methods . Leonard Bickman and Debra J. Rog, eds. 2nd ed. (Thousand Oaks, CA: Sage, 2009), p. 214-253; Qualitative Research Methods. Writing@CSU. Colorado State University; Yin, Robert K. Qualitative Research from Start to Finish . 2nd edition. New York: Guilford, 2015.

Strengths of Using Qualitative Methods

The advantage of using qualitative methods is that they generate rich, detailed data that leave the participants' perspectives intact and provide multiple contexts for understanding the phenomenon under study. In this way, qualitative research can be used to vividly demonstrate phenomena or to conduct cross-case comparisons and analysis of individuals or groups.

Among the specific strengths of using qualitative methods to study social science research problems is the ability to:

• Obtain a more realistic view of the lived world that cannot be understood or experienced in numerical data and statistical analysis;
• Provide the researcher with the perspective of the participants of the study through immersion in a culture or situation and as a result of direct interaction with them;
• Allow the researcher to describe existing phenomena and current situations;
• Develop flexible ways to perform data collection, subsequent analysis, and interpretation of collected information;
• Yield results that can be helpful in pioneering new ways of understanding;
• Respond to changes that occur while conducting the study ]e.g., extended fieldwork or observation] and offer the flexibility to shift the focus of the research as a result;
• Provide a holistic view of the phenomena under investigation;
• Respond to local situations, conditions, and needs of participants;
• Interact with the research subjects in their own language and on their own terms; and,
• Create a descriptive capability based on primary and unstructured data.

Anderson, Claire. “Presenting and Evaluating Qualitative Research.” American Journal of Pharmaceutical Education 74 (2010): 1-7; Denzin, Norman. K. and Yvonna S. Lincoln. Handbook of Qualitative Research . 2nd edition. Thousand Oaks, CA: Sage, 2000; Merriam, Sharan B. Qualitative Research: A Guide to Design and Implementation . San Francisco, CA: Jossey-Bass, 2009.

Limitations of Using Qualitative Methods

It is very much true that most of the limitations you find in using qualitative research techniques also reflect their inherent strengths . For example, small sample sizes help you investigate research problems in a comprehensive and in-depth manner. However, small sample sizes undermine opportunities to draw useful generalizations from, or to make broad policy recommendations based upon, the findings. Additionally, as the primary instrument of investigation, qualitative researchers are often embedded in the cultures and experiences of others. However, cultural embeddedness increases the opportunity for bias generated from conscious or unconscious assumptions about the study setting to enter into how data is gathered, interpreted, and reported.

Some specific limitations associated with using qualitative methods to study research problems in the social sciences include the following:

• Drifting away from the original objectives of the study in response to the changing nature of the context under which the research is conducted;
• Arriving at different conclusions based on the same information depending on the personal characteristics of the researcher;
• Replication of a study is very difficult;
• Research using human subjects increases the chance of ethical dilemmas that undermine the overall validity of the study;
• An inability to investigate causality between different research phenomena;
• Difficulty in explaining differences in the quality and quantity of information obtained from different respondents and arriving at different, non-consistent conclusions;
• Data gathering and analysis is often time consuming and/or expensive;
• Requires a high level of experience from the researcher to obtain the targeted information from the respondent;
• May lack consistency and reliability because the researcher can employ different probing techniques and the respondent can choose to tell some particular stories and ignore others; and,
• Generation of a significant amount of data that cannot be randomized into manageable parts for analysis.

Research Tip

Human Subject Research and Institutional Review Board Approval

Almost every socio-behavioral study requires you to submit your proposed research plan to an Institutional Review Board. The role of the Board is to evaluate your research proposal and determine whether it will be conducted ethically and under the regulations, institutional polices, and Code of Ethics set forth by the university. The purpose of the review is to protect the rights and welfare of individuals participating in your study. The review is intended to ensure equitable selection of respondents, that you have met the requirements for obtaining informed consent , that there is clear assessment and minimization of risks to participants and to the university [read: no lawsuits!], and that privacy and confidentiality are maintained throughout the research process and beyond. Go to the USC IRB website for detailed information and templates of forms you need to submit before you can proceed. If you are  unsure whether your study is subject to IRB review, consult with your professor or academic advisor.

Chenail, Ronald J. Introduction to Qualitative Research Design. Nova Southeastern University; Labaree, Robert V. "Working Successfully with Your Institutional Review Board: Practical Advice for Academic Librarians." College and Research Libraries News 71 (April 2010): 190-193.

Another Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

NOTE :  For a list of online communities, research centers, indispensable learning resources, and personal websites of leading qualitative researchers, GO HERE .

For a list of scholarly journals devoted to the study and application of qualitative research methods, GO HERE .

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World Leaders in Research-Based User Experience

Why 5 participants are okay in a qualitative study, but not in a quantitative one.

July 11, 2021 2021-07-11

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In our quantitative-usability classes ( Measuring UX and ROI and Statistics for UX ) we often recommend a sizeable number of participants for quantitative studies — usually more than 30. We’ve said again and again that metrics collected in qualitative usability testing are often misleading and do not generalize to the general population. (There could be exceptions, but you always need to check by calculating confidence intervals and statistical significance ). And, almost inevitably, the retort comes back — Didn’t Jakob Nielsen recommend 5 users for usability studies ? If you need more users for statistical reasons, then it certainly means that the results obtained with 5 users aren’t valid, doesn’t it?

This question is so frequent, that we need to address the misunderstanding.

In This Article:

Quantitative usability studies: more than 5 participants, qualitative usability studies: assumptions behind the 5-user guideline, questioning the assumptions behind the 5-user guideline.

Quantitative usability studies are usually summative in nature: their goal is to measure the usability of a system (site, application, or some other product), arriving at one or more numbers. These studies attempt to get a sense of how good an interface is for its users by looking at a variety of metrics: how many users from the general population can complete one or more top tasks, how long it takes them, how many errors they make, and how satisfied they are with their experience. They usually involve collecting values for each participant, aggregating those values in summary statistics such as averages or success rates, calculating confidence intervals for those aggregates, and reporting likely ranges for the true score for the whole population. The results of such a study may indicate that the success rate for a top task for the whole population is somewhere between 75% and 90%, with a 95% confidence level and that the task time is between 2.3 and 2.6 minutes. These ranges (in effect, confidence intervals) should be fairly narrow to convey any interesting information (knowing that a success rate is between 5% and 95% is not very helpful, is it?), and they usually are narrow only if you include a large number of participants (40 or more).  Hence, the recommendation to calculate confidence intervals for all metrics collected and not to rely on summary statistics when studies contain just a few users.

In contrast, qualitative user studies are mostly formative : their goal is to figure out what doesn’t work in a design, fix it, and then move on with a new, better version. The new version will usually also get tested, improved on, and so forth. While it is possible to have qualitative studies that have summative goals (let’s see all that’s wrong with our current website!), a lot of the times they simply aim to refine an existing design iteration . Qualitative studies (even when they are summative) do not try to predict how many users will complete a task, nor do they attempt to figure out how many people will run into any specific usability issue. They are meant to identify usability problems.

In comes Jakob Nielsen’s article that recommends qualitative testing with 5 users. There are three main assumptions behind that recommendation:

• That you are trying to identify issues in a design . By definition, an issue is some usability problem that the user experiences while using the design.
• That any issue that somebody encounters is a valid one worth fixing.  To make an  analogy for this assumption : if one person falls into a pothole, you know you need to fix it. You don’t need 100 people to fall into it to decide it needs fixing.
• That the probability of someone encountering an issue is 31%

Based on these assumptions, Jakob Nielsen and Tom Landauer built a mathematical model that shows that, by doing a qualitative test with 5 participants, you will identify 85% of the issues in an interface. And Jakob Nielsen has repeatedly argued (and justly so) that a good investment is to start with 5 people, find your 85% of the issues, fix them, then test again with another 5 people, and so on. It’s not worth trying to find all the issues in one test because you’ll spend too much time and money, and then you’ll be sure to introduce other issues in the redesign.

Note that the “metrics” collected in the quantitative and qualitative studies are very different: in quantitative studies you’re interested in how your general population will fare on measures such as task success, errors, satisfaction, and task time . In qualitative studies, you’re simply counting usability issues . And, while there is a statistical uncertainty about any number obtained from a quantitative study (how will the average obtained from my study compare with the average of the general population), there is absolutely no uncertainty in a qualitative study — any error identified is a legit problem that needs to be fixed.

I gave you a list of assumptions on which the 5-user guideline is based. However, you may not agree with (some of) them. I don’t think there’s much to argue about the first assumption, but you may bring some valid objections to the second and third one.

Does any error that someone encounters need to be fixed? One may argue that if 1000 out of a 1000 people fall into a pothole, you do need to repair it, but not if only one person out of 1000 falls into it. With a qualitative usability study, you have no guarantee (based on only the study) that an identified issue is likely to be encountered by more users than the ones that happened to come to your study. So, in that sense, the results cannot be generalized to the whole population.

Yes, if you wanted you could run a quantitative study to predict how many people in the general population are likely to encounter a particular error. And, then, yes, you could prioritize errors based on how likely they are and fix the ones with the highest priority. While that approach is certainly very sound, it’s probably going to be also very wasteful — you will need to test your design with a pretty large number of users to identify its main problems, then fix them, and introduce another ones that will need to be identified and prioritized.

Instead, the qualitative approach assumes that designers will use some other means to prioritize among different issues — maybe some of them are too expensive to fix or others are related to a functionality that only few of your users are likely to use. Qualitative user testing simply gives you a list of problems . It is the researcher job’s to prioritize among the different issues and move on.

Is the chance of encountering a problem in an interface 31%? The 31% number was based on an average across several projects run in the early 90s. It is possible that, since then, the chance of encountering an issue has changed. It’s also possible that, as you’re doing more design iterations and fixing more and more errors, the usability of your product is substantially better and, in fact, it’s more difficult to encounter new issues.

The good news is that the chance of encountering an error in an interface is only a parameter in Nielsen and Landauer’s model. So, if you know that your interface is pretty good, you can simply insert your desired probability in that model. The number of users will be given by this equation:

where L is your estimated probability of encountering an error in an interface, expressed as a decimal (i.e., 31% is entered as .31)

For example, if L is 20%, you would need 9 users to find 85% of the problems in the interface. If L is 10%, then you’d need 18 users. The more usable your interface is, the more users you need to include in the test to identify 85% of the usability problems.

However, your real goal is not to find a particular percentage of problems, but to maximize the business value of your user-research program . It turns out that peak ROI is fairly insensitive to variations in model parameters. If you are testing a terrible design for the very first time, your expenses will be low (it will be very easy to identify usability problems) and your gains will be high (the product will be hugely improved). Conversely, if you are researching a difficult problem, your expenses will be higher and your gains will be lower. However, the point that maximizes the ratio between gains and expenses (i.e., ROI) will still usually be around 5 test users, even though your study profitability will be higher for the easy study and lower for the harder study.

In general, it’s a good idea to start with 5 users, fix the errors that you find, and then slowly increase the number of users on further iterations if you think that you’ve made great progress. But, in practice, you can easily get a sense of how much insight you’ve found with 5 users. If you feel that not much, by all means, include a few additional users. Conversely, you can test with fewer than 5 users under other circumstances, such as when you can proceed to testing the next iteration very quickly. But if you have plenty of issues that you need to work on, first fix those, then move on.

There is no contradiction between the 5-user guideline for qualitative user testing and the idea that you cannot trust metrics obtained from small studies , because you do not collect metrics in a qualitative study. Quantitative and qualitative user studies have different goals :

• Quantitative studies aim to find metrics that predict the behavior of the whole population; such numbers will be imprecise — and thus useless — if they are based on a small sample size.
• Qualitative studies aim for insights : to identify usability issues in an interface. Researchers must use judgment rather than numbers to prioritize these issues. (And, to hammer home the point: the 5-user guideline only applies to qualitative, not to quantitative studies.]

If your interface has already gone through many rounds of testing, you may need to include more people even in a qualitative test, as the chance of encountering a problem may be smaller than the original assumptions of the model. Still, it’s good practice to start with 5 users and then increase the number if there are too few important findings.

Related Courses

Measuring ux and roi.

Use metrics from quantitative research to demonstrate value

How to Interpret UX Numbers: Statistics for UX

When research data should be trusted; what statistics to use when

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Chapter 11. Interviewing

Introduction.

Interviewing people is at the heart of qualitative research. It is not merely a way to collect data but an intrinsically rewarding activity—an interaction between two people that holds the potential for greater understanding and interpersonal development. Unlike many of our daily interactions with others that are fairly shallow and mundane, sitting down with a person for an hour or two and really listening to what they have to say is a profound and deep enterprise, one that can provide not only “data” for you, the interviewer, but also self-understanding and a feeling of being heard for the interviewee. I always approach interviewing with a deep appreciation for the opportunity it gives me to understand how other people experience the world. That said, there is not one kind of interview but many, and some of these are shallower than others. This chapter will provide you with an overview of interview techniques but with a special focus on the in-depth semistructured interview guide approach, which is the approach most widely used in social science research.

An interview can be variously defined as “a conversation with a purpose” ( Lune and Berg 2018 ) and an attempt to understand the world from the point of view of the person being interviewed: “to unfold the meaning of peoples’ experiences, to uncover their lived world prior to scientific explanations” ( Kvale 2007 ). It is a form of active listening in which the interviewer steers the conversation to subjects and topics of interest to their research but also manages to leave enough space for those interviewed to say surprising things. Achieving that balance is a tricky thing, which is why most practitioners believe interviewing is both an art and a science. In my experience as a teacher, there are some students who are “natural” interviewers (often they are introverts), but anyone can learn to conduct interviews, and everyone, even those of us who have been doing this for years, can improve their interviewing skills. This might be a good time to highlight the fact that the interview is a product between interviewer and interviewee and that this product is only as good as the rapport established between the two participants. Active listening is the key to establishing this necessary rapport.

Patton ( 2002 ) makes the argument that we use interviews because there are certain things that are not observable. In particular, “we cannot observe feelings, thoughts, and intentions. We cannot observe behaviors that took place at some previous point in time. We cannot observe situations that preclude the presence of an observer. We cannot observe how people have organized the world and the meanings they attach to what goes on in the world. We have to ask people questions about those things” ( 341 ).

Types of Interviews

There are several distinct types of interviews. Imagine a continuum (figure 11.1). On one side are unstructured conversations—the kind you have with your friends. No one is in control of those conversations, and what you talk about is often random—whatever pops into your head. There is no secret, underlying purpose to your talking—if anything, the purpose is to talk to and engage with each other, and the words you use and the things you talk about are a little beside the point. An unstructured interview is a little like this informal conversation, except that one of the parties to the conversation (you, the researcher) does have an underlying purpose, and that is to understand the other person. You are not friends speaking for no purpose, but it might feel just as unstructured to the “interviewee” in this scenario. That is one side of the continuum. On the other side are fully structured and standardized survey-type questions asked face-to-face. Here it is very clear who is asking the questions and who is answering them. This doesn’t feel like a conversation at all! A lot of people new to interviewing have this ( erroneously !) in mind when they think about interviews as data collection. Somewhere in the middle of these two extreme cases is the “ semistructured” interview , in which the researcher uses an “interview guide” to gently move the conversation to certain topics and issues. This is the primary form of interviewing for qualitative social scientists and will be what I refer to as interviewing for the rest of this chapter, unless otherwise specified.

Informal (unstructured conversations). This is the most “open-ended” approach to interviewing. It is particularly useful in conjunction with observational methods (see chapters 13 and 14). There are no predetermined questions. Each interview will be different. Imagine you are researching the Oregon Country Fair, an annual event in Veneta, Oregon, that includes live music, artisan craft booths, face painting, and a lot of people walking through forest paths. It’s unlikely that you will be able to get a person to sit down with you and talk intensely about a set of questions for an hour and a half. But you might be able to sidle up to several people and engage with them about their experiences at the fair. You might have a general interest in what attracts people to these events, so you could start a conversation by asking strangers why they are here or why they come back every year. That’s it. Then you have a conversation that may lead you anywhere. Maybe one person tells a long story about how their parents brought them here when they were a kid. A second person talks about how this is better than Burning Man. A third person shares their favorite traveling band. And yet another enthuses about the public library in the woods. During your conversations, you also talk about a lot of other things—the weather, the utilikilts for sale, the fact that a favorite food booth has disappeared. It’s all good. You may not be able to record these conversations. Instead, you might jot down notes on the spot and then, when you have the time, write down as much as you can remember about the conversations in long fieldnotes. Later, you will have to sit down with these fieldnotes and try to make sense of all the information (see chapters 18 and 19).

Interview guide ( semistructured interview ). This is the primary type employed by social science qualitative researchers. The researcher creates an “interview guide” in advance, which she uses in every interview. In theory, every person interviewed is asked the same questions. In practice, every person interviewed is asked mostly the same topics but not always the same questions, as the whole point of a “guide” is that it guides the direction of the conversation but does not command it. The guide is typically between five and ten questions or question areas, sometimes with suggested follow-ups or prompts . For example, one question might be “What was it like growing up in Eastern Oregon?” with prompts such as “Did you live in a rural area? What kind of high school did you attend?” to help the conversation develop. These interviews generally take place in a quiet place (not a busy walkway during a festival) and are recorded. The recordings are transcribed, and those transcriptions then become the “data” that is analyzed (see chapters 18 and 19). The conventional length of one of these types of interviews is between one hour and two hours, optimally ninety minutes. Less than one hour doesn’t allow for much development of questions and thoughts, and two hours (or more) is a lot of time to ask someone to sit still and answer questions. If you have a lot of ground to cover, and the person is willing, I highly recommend two separate interview sessions, with the second session being slightly shorter than the first (e.g., ninety minutes the first day, sixty minutes the second). There are lots of good reasons for this, but the most compelling one is that this allows you to listen to the first day’s recording and catch anything interesting you might have missed in the moment and so develop follow-up questions that can probe further. This also allows the person being interviewed to have some time to think about the issues raised in the interview and go a little deeper with their answers.

Standardized questionnaire with open responses ( structured interview ). This is the type of interview a lot of people have in mind when they hear “interview”: a researcher comes to your door with a clipboard and proceeds to ask you a series of questions. These questions are all the same whoever answers the door; they are “standardized.” Both the wording and the exact order are important, as people’s responses may vary depending on how and when a question is asked. These are qualitative only in that the questions allow for “open-ended responses”: people can say whatever they want rather than select from a predetermined menu of responses. For example, a survey I collaborated on included this open-ended response question: “How does class affect one’s career success in sociology?” Some of the answers were simply one word long (e.g., “debt”), and others were long statements with stories and personal anecdotes. It is possible to be surprised by the responses. Although it’s a stretch to call this kind of questioning a conversation, it does allow the person answering the question some degree of freedom in how they answer.

Survey questionnaire with closed responses (not an interview!). Standardized survey questions with specific answer options (e.g., closed responses) are not really interviews at all, and they do not generate qualitative data. For example, if we included five options for the question “How does class affect one’s career success in sociology?”—(1) debt, (2) social networks, (3) alienation, (4) family doesn’t understand, (5) type of grad program—we leave no room for surprises at all. Instead, we would most likely look at patterns around these responses, thinking quantitatively rather than qualitatively (e.g., using regression analysis techniques, we might find that working-class sociologists were twice as likely to bring up alienation). It can sometimes be confusing for new students because the very same survey can include both closed-ended and open-ended questions. The key is to think about how these will be analyzed and to what level surprises are possible. If your plan is to turn all responses into a number and make predictions about correlations and relationships, you are no longer conducting qualitative research. This is true even if you are conducting this survey face-to-face with a real live human. Closed-response questions are not conversations of any kind, purposeful or not.

In summary, the semistructured interview guide approach is the predominant form of interviewing for social science qualitative researchers because it allows a high degree of freedom of responses from those interviewed (thus allowing for novel discoveries) while still maintaining some connection to a research question area or topic of interest. The rest of the chapter assumes the employment of this form.

Creating an Interview Guide

Your interview guide is the instrument used to bridge your research question(s) and what the people you are interviewing want to tell you. Unlike a standardized questionnaire, the questions actually asked do not need to be exactly what you have written down in your guide. The guide is meant to create space for those you are interviewing to talk about the phenomenon of interest, but sometimes you are not even sure what that phenomenon is until you start asking questions. A priority in creating an interview guide is to ensure it offers space. One of the worst mistakes is to create questions that are so specific that the person answering them will not stray. Relatedly, questions that sound “academic” will shut down a lot of respondents. A good interview guide invites respondents to talk about what is important to them, not feel like they are performing or being evaluated by you.

Good interview questions should not sound like your “research question” at all. For example, let’s say your research question is “How do patriarchal assumptions influence men’s understanding of climate change and responses to climate change?” It would be worse than unhelpful to ask a respondent, “How do your assumptions about the role of men affect your understanding of climate change?” You need to unpack this into manageable nuggets that pull your respondent into the area of interest without leading him anywhere. You could start by asking him what he thinks about climate change in general. Or, even better, whether he has any concerns about heatwaves or increased tornadoes or polar icecaps melting. Once he starts talking about that, you can ask follow-up questions that bring in issues around gendered roles, perhaps asking if he is married (to a woman) and whether his wife shares his thoughts and, if not, how they negotiate that difference. The fact is, you won’t really know the right questions to ask until he starts talking.

There are several distinct types of questions that can be used in your interview guide, either as main questions or as follow-up probes. If you remember that the point is to leave space for the respondent, you will craft a much more effective interview guide! You will also want to think about the place of time in both the questions themselves (past, present, future orientations) and the sequencing of the questions.

Researcher Note

Suggestion : As you read the next three sections (types of questions, temporality, question sequence), have in mind a particular research question, and try to draft questions and sequence them in a way that opens space for a discussion that helps you answer your research question.

Type of Questions

Experience and behavior questions ask about what a respondent does regularly (their behavior) or has done (their experience). These are relatively easy questions for people to answer because they appear more “factual” and less subjective. This makes them good opening questions. For the study on climate change above, you might ask, “Have you ever experienced an unusual weather event? What happened?” Or “You said you work outside? What is a typical summer workday like for you? How do you protect yourself from the heat?”

Opinion and values questions , in contrast, ask questions that get inside the minds of those you are interviewing. “Do you think climate change is real? Who or what is responsible for it?” are two such questions. Note that you don’t have to literally ask, “What is your opinion of X?” but you can find a way to ask the specific question relevant to the conversation you are having. These questions are a bit trickier to ask because the answers you get may depend in part on how your respondent perceives you and whether they want to please you or not. We’ve talked a fair amount about being reflective. Here is another place where this comes into play. You need to be aware of the effect your presence might have on the answers you are receiving and adjust accordingly. If you are a woman who is perceived as liberal asking a man who identifies as conservative about climate change, there is a lot of subtext that can be going on in the interview. There is no one right way to resolve this, but you must at least be aware of it.

Feeling questions are questions that ask respondents to draw on their emotional responses. It’s pretty common for academic researchers to forget that we have bodies and emotions, but people’s understandings of the world often operate at this affective level, sometimes unconsciously or barely consciously. It is a good idea to include questions that leave space for respondents to remember, imagine, or relive emotional responses to particular phenomena. “What was it like when you heard your cousin’s house burned down in that wildfire?” doesn’t explicitly use any emotion words, but it allows your respondent to remember what was probably a pretty emotional day. And if they respond emotionally neutral, that is pretty interesting data too. Note that asking someone “How do you feel about X” is not always going to evoke an emotional response, as they might simply turn around and respond with “I think that…” It is better to craft a question that actually pushes the respondent into the affective category. This might be a specific follow-up to an experience and behavior question —for example, “You just told me about your daily routine during the summer heat. Do you worry it is going to get worse?” or “Have you ever been afraid it will be too hot to get your work accomplished?”

Knowledge questions ask respondents what they actually know about something factual. We have to be careful when we ask these types of questions so that respondents do not feel like we are evaluating them (which would shut them down), but, for example, it is helpful to know when you are having a conversation about climate change that your respondent does in fact know that unusual weather events have increased and that these have been attributed to climate change! Asking these questions can set the stage for deeper questions and can ensure that the conversation makes the same kind of sense to both participants. For example, a conversation about political polarization can be put back on track once you realize that the respondent doesn’t really have a clear understanding that there are two parties in the US. Instead of asking a series of questions about Republicans and Democrats, you might shift your questions to talk more generally about political disagreements (e.g., “people against abortion”). And sometimes what you do want to know is the level of knowledge about a particular program or event (e.g., “Are you aware you can discharge your student loans through the Public Service Loan Forgiveness program?”).

Sensory questions call on all senses of the respondent to capture deeper responses. These are particularly helpful in sparking memory. “Think back to your childhood in Eastern Oregon. Describe the smells, the sounds…” Or you could use these questions to help a person access the full experience of a setting they customarily inhabit: “When you walk through the doors to your office building, what do you see? Hear? Smell?” As with feeling questions , these questions often supplement experience and behavior questions . They are another way of allowing your respondent to report fully and deeply rather than remain on the surface.

Creative questions employ illustrative examples, suggested scenarios, or simulations to get respondents to think more deeply about an issue, topic, or experience. There are many options here. In The Trouble with Passion , Erin Cech ( 2021 ) provides a scenario in which “Joe” is trying to decide whether to stay at his decent but boring computer job or follow his passion by opening a restaurant. She asks respondents, “What should Joe do?” Their answers illuminate the attraction of “passion” in job selection. In my own work, I have used a news story about an upwardly mobile young man who no longer has time to see his mother and sisters to probe respondents’ feelings about the costs of social mobility. Jessi Streib and Betsy Leondar-Wright have used single-page cartoon “scenes” to elicit evaluations of potential racial discrimination, sexual harassment, and classism. Barbara Sutton ( 2010 ) has employed lists of words (“strong,” “mother,” “victim”) on notecards she fans out and asks her female respondents to select and discuss.

Background/Demographic Questions

You most definitely will want to know more about the person you are interviewing in terms of conventional demographic information, such as age, race, gender identity, occupation, and educational attainment. These are not questions that normally open up inquiry. [1] For this reason, my practice has been to include a separate “demographic questionnaire” sheet that I ask each respondent to fill out at the conclusion of the interview. Only include those aspects that are relevant to your study. For example, if you are not exploring religion or religious affiliation, do not include questions about a person’s religion on the demographic sheet. See the example provided at the end of this chapter.

Temporality

Any type of question can have a past, present, or future orientation. For example, if you are asking a behavior question about workplace routine, you might ask the respondent to talk about past work, present work, and ideal (future) work. Similarly, if you want to understand how people cope with natural disasters, you might ask your respondent how they felt then during the wildfire and now in retrospect and whether and to what extent they have concerns for future wildfire disasters. It’s a relatively simple suggestion—don’t forget to ask about past, present, and future—but it can have a big impact on the quality of the responses you receive.

Question Sequence

Having a list of good questions or good question areas is not enough to make a good interview guide. You will want to pay attention to the order in which you ask your questions. Even though any one respondent can derail this order (perhaps by jumping to answer a question you haven’t yet asked), a good advance plan is always helpful. When thinking about sequence, remember that your goal is to get your respondent to open up to you and to say things that might surprise you. To establish rapport, it is best to start with nonthreatening questions. Asking about the present is often the safest place to begin, followed by the past (they have to know you a little bit to get there), and lastly, the future (talking about hopes and fears requires the most rapport). To allow for surprises, it is best to move from very general questions to more particular questions only later in the interview. This ensures that respondents have the freedom to bring up the topics that are relevant to them rather than feel like they are constrained to answer you narrowly. For example, refrain from asking about particular emotions until these have come up previously—don’t lead with them. Often, your more particular questions will emerge only during the course of the interview, tailored to what is emerging in conversation.

Once you have a set of questions, read through them aloud and imagine you are being asked the same questions. Does the set of questions have a natural flow? Would you be willing to answer the very first question to a total stranger? Does your sequence establish facts and experiences before moving on to opinions and values? Did you include prefatory statements, where necessary; transitions; and other announcements? These can be as simple as “Hey, we talked a lot about your experiences as a barista while in college.… Now I am turning to something completely different: how you managed friendships in college.” That is an abrupt transition, but it has been softened by your acknowledgment of that.

Probes and Flexibility

Once you have the interview guide, you will also want to leave room for probes and follow-up questions. As in the sample probe included here, you can write out the obvious probes and follow-up questions in advance. You might not need them, as your respondent might anticipate them and include full responses to the original question. Or you might need to tailor them to how your respondent answered the question. Some common probes and follow-up questions include asking for more details (When did that happen? Who else was there?), asking for elaboration (Could you say more about that?), asking for clarification (Does that mean what I think it means or something else? I understand what you mean, but someone else reading the transcript might not), and asking for contrast or comparison (How did this experience compare with last year’s event?). “Probing is a skill that comes from knowing what to look for in the interview, listening carefully to what is being said and what is not said, and being sensitive to the feedback needs of the person being interviewed” ( Patton 2002:374 ). It takes work! And energy. I and many other interviewers I know report feeling emotionally and even physically drained after conducting an interview. You are tasked with active listening and rearranging your interview guide as needed on the fly. If you only ask the questions written down in your interview guide with no deviations, you are doing it wrong. [2]

The Final Question

Every interview guide should include a very open-ended final question that allows for the respondent to say whatever it is they have been dying to tell you but you’ve forgotten to ask. About half the time they are tired too and will tell you they have nothing else to say. But incredibly, some of the most honest and complete responses take place here, at the end of a long interview. You have to realize that the person being interviewed is often discovering things about themselves as they talk to you and that this process of discovery can lead to new insights for them. Making space at the end is therefore crucial. Be sure you convey that you actually do want them to tell you more, that the offer of “anything else?” is not read as an empty convention where the polite response is no. Here is where you can pull from that active listening and tailor the final question to the particular person. For example, “I’ve asked you a lot of questions about what it was like to live through that wildfire. I’m wondering if there is anything I’ve forgotten to ask, especially because I haven’t had that experience myself” is a much more inviting final question than “Great. Anything you want to add?” It’s also helpful to convey to the person that you have the time to listen to their full answer, even if the allotted time is at the end. After all, there are no more questions to ask, so the respondent knows exactly how much time is left. Do them the courtesy of listening to them!

Conducting the Interview

Once you have your interview guide, you are on your way to conducting your first interview. I always practice my interview guide with a friend or family member. I do this even when the questions don’t make perfect sense for them, as it still helps me realize which questions make no sense, are poorly worded (too academic), or don’t follow sequentially. I also practice the routine I will use for interviewing, which goes something like this:

• Introduce myself and reintroduce the study
• Provide consent form and ask them to sign and retain/return copy
• Ask if they have any questions about the study before we begin
• Ask if I can begin recording
• Ask questions (from interview guide)
• Turn off the recording device
• Ask if they are willing to fill out my demographic questionnaire
• Collect questionnaire and, without looking at the answers, place in same folder as signed consent form
• Thank them and depart

A note on remote interviewing: Interviews have traditionally been conducted face-to-face in a private or quiet public setting. You don’t want a lot of background noise, as this will make transcriptions difficult. During the recent global pandemic, many interviewers, myself included, learned the benefits of interviewing remotely. Although face-to-face is still preferable for many reasons, Zoom interviewing is not a bad alternative, and it does allow more interviews across great distances. Zoom also includes automatic transcription, which significantly cuts down on the time it normally takes to convert our conversations into “data” to be analyzed. These automatic transcriptions are not perfect, however, and you will still need to listen to the recording and clarify and clean up the transcription. Nor do automatic transcriptions include notations of body language or change of tone, which you may want to include. When interviewing remotely, you will want to collect the consent form before you meet: ask them to read, sign, and return it as an email attachment. I think it is better to ask for the demographic questionnaire after the interview, but because some respondents may never return it then, it is probably best to ask for this at the same time as the consent form, in advance of the interview.

What should you bring to the interview? I would recommend bringing two copies of the consent form (one for you and one for the respondent), a demographic questionnaire, a manila folder in which to place the signed consent form and filled-out demographic questionnaire, a printed copy of your interview guide (I print with three-inch right margins so I can jot down notes on the page next to relevant questions), a pen, a recording device, and water.

After the interview, you will want to secure the signed consent form in a locked filing cabinet (if in print) or a password-protected folder on your computer. Using Excel or a similar program that allows tables/spreadsheets, create an identifying number for your interview that links to the consent form without using the name of your respondent. For example, let’s say that I conduct interviews with US politicians, and the first person I meet with is George W. Bush. I will assign the transcription the number “INT#001” and add it to the signed consent form. [3] The signed consent form goes into a locked filing cabinet, and I never use the name “George W. Bush” again. I take the information from the demographic sheet, open my Excel spreadsheet, and add the relevant information in separate columns for the row INT#001: White, male, Republican. When I interview Bill Clinton as my second interview, I include a second row: INT#002: White, male, Democrat. And so on. The only link to the actual name of the respondent and this information is the fact that the consent form (unavailable to anyone but me) has stamped on it the interview number.

Many students get very nervous before their first interview. Actually, many of us are always nervous before the interview! But do not worry—this is normal, and it does pass. Chances are, you will be pleasantly surprised at how comfortable it begins to feel. These “purposeful conversations” are often a delight for both participants. This is not to say that sometimes things go wrong. I often have my students practice several “bad scenarios” (e.g., a respondent that you cannot get to open up; a respondent who is too talkative and dominates the conversation, steering it away from the topics you are interested in; emotions that completely take over; or shocking disclosures you are ill-prepared to handle), but most of the time, things go quite well. Be prepared for the unexpected, but know that the reason interviews are so popular as a technique of data collection is that they are usually richly rewarding for both participants.

One thing that I stress to my methods students and remind myself about is that interviews are still conversations between people. If there’s something you might feel uncomfortable asking someone about in a “normal” conversation, you will likely also feel a bit of discomfort asking it in an interview. Maybe more importantly, your respondent may feel uncomfortable. Social research—especially about inequality—can be uncomfortable. And it’s easy to slip into an abstract, intellectualized, or removed perspective as an interviewer. This is one reason trying out interview questions is important. Another is that sometimes the question sounds good in your head but doesn’t work as well out loud in practice. I learned this the hard way when a respondent asked me how I would answer the question I had just posed, and I realized that not only did I not really know how I would answer it, but I also wasn’t quite as sure I knew what I was asking as I had thought.

—Elizabeth M. Lee, Associate Professor of Sociology at Saint Joseph’s University, author of Class and Campus Life , and co-author of Geographies of Campus Inequality

How Many Interviews?

Your research design has included a targeted number of interviews and a recruitment plan (see chapter 5). Follow your plan, but remember that “ saturation ” is your goal. You interview as many people as you can until you reach a point at which you are no longer surprised by what they tell you. This means not that no one after your first twenty interviews will have surprising, interesting stories to tell you but rather that the picture you are forming about the phenomenon of interest to you from a research perspective has come into focus, and none of the interviews are substantially refocusing that picture. That is when you should stop collecting interviews. Note that to know when you have reached this, you will need to read your transcripts as you go. More about this in chapters 18 and 19.

Your Final Product: The Ideal Interview Transcript

A good interview transcript will demonstrate a subtly controlled conversation by the skillful interviewer. In general, you want to see replies that are about one paragraph long, not short sentences and not running on for several pages. Although it is sometimes necessary to follow respondents down tangents, it is also often necessary to pull them back to the questions that form the basis of your research study. This is not really a free conversation, although it may feel like that to the person you are interviewing.

Final Tips from an Interview Master

Annette Lareau is arguably one of the masters of the trade. In Listening to People , she provides several guidelines for good interviews and then offers a detailed example of an interview gone wrong and how it could be addressed (please see the “Further Readings” at the end of this chapter). Here is an abbreviated version of her set of guidelines: (1) interview respondents who are experts on the subjects of most interest to you (as a corollary, don’t ask people about things they don’t know); (2) listen carefully and talk as little as possible; (3) keep in mind what you want to know and why you want to know it; (4) be a proactive interviewer (subtly guide the conversation); (5) assure respondents that there aren’t any right or wrong answers; (6) use the respondent’s own words to probe further (this both allows you to accurately identify what you heard and pushes the respondent to explain further); (7) reuse effective probes (don’t reinvent the wheel as you go—if repeating the words back works, do it again and again); (8) focus on learning the subjective meanings that events or experiences have for a respondent; (9) don’t be afraid to ask a question that draws on your own knowledge (unlike trial lawyers who are trained never to ask a question for which they don’t already know the answer, sometimes it’s worth it to ask risky questions based on your hypotheses or just plain hunches); (10) keep thinking while you are listening (so difficult…and important); (11) return to a theme raised by a respondent if you want further information; (12) be mindful of power inequalities (and never ever coerce a respondent to continue the interview if they want out); (13) take control with overly talkative respondents; (14) expect overly succinct responses, and develop strategies for probing further; (15) balance digging deep and moving on; (16) develop a plan to deflect questions (e.g., let them know you are happy to answer any questions at the end of the interview, but you don’t want to take time away from them now); and at the end, (17) check to see whether you have asked all your questions. You don’t always have to ask everyone the same set of questions, but if there is a big area you have forgotten to cover, now is the time to recover ( Lareau 2021:93–103 ).

Sample: Demographic Questionnaire

ASA Taskforce on First-Generation and Working-Class Persons in Sociology – Class Effects on Career Success

Supplementary Demographic Questionnaire

Thank you for your participation in this interview project. We would like to collect a few pieces of key demographic information from you to supplement our analyses. Your answers to these questions will be kept confidential and stored by ID number. All of your responses here are entirely voluntary!

What best captures your race/ethnicity? (please check any/all that apply)

• White (Non Hispanic/Latina/o/x)
• Black or African American
• Hispanic, Latino/a/x of Spanish
• Asian or Asian American
• American Indian or Alaska Native
• Middle Eastern or North African
• Native Hawaiian or Pacific Islander
• Other : (Please write in: ________________)

What is your current position?

• Grad Student
• Full Professor

Please check any and all of the following that apply to you:

• I identify as a working-class academic
• I was the first in my family to graduate from college
• I grew up poor

What best reflects your gender?

• Transgender female/Transgender woman
• Transgender male/Transgender man
• Gender queer/ Gender nonconforming

Anything else you would like us to know about you?

Example: Interview Guide

In this example, follow-up prompts are italicized.  Note the sequence of questions.  That second question often elicits an entire life history , answering several later questions in advance.

Introduction Script/Question

Thank you for participating in our survey of ASA members who identify as first-generation or working-class.  As you may have heard, ASA has sponsored a taskforce on first-generation and working-class persons in sociology and we are interested in hearing from those who so identify.  Your participation in this interview will help advance our knowledge in this area.

• The first thing we would like to as you is why you have volunteered to be part of this study? What does it mean to you be first-gen or working class?  Why were you willing to be interviewed?
• How did you decide to become a sociologist?
• Can you tell me a little bit about where you grew up? ( prompts: what did your parent(s) do for a living?  What kind of high school did you attend?)
• Has this identity been salient to your experience? (how? How much?)
• How welcoming was your grad program? Your first academic employer?
• Why did you decide to pursue sociology at the graduate level?
• Did you experience culture shock in college? In graduate school?
• Has your FGWC status shaped how you’ve thought about where you went to school? debt? etc?
• Were you mentored? How did this work (not work)?  How might it?
• What did you consider when deciding where to go to grad school? Where to apply for your first position?
• What, to you, is a mark of career success? Have you achieved that success?  What has helped or hindered your pursuit of success?
• Do you think sociology, as a field, cares about prestige?
• Let’s talk a little bit about intersectionality. How does being first-gen/working class work alongside other identities that are important to you?
• What do your friends and family think about your career? Have you had any difficulty relating to family members or past friends since becoming highly educated?
• Do you have any debt from college/grad school? Are you concerned about this?  Could you explain more about how you paid for college/grad school?  (here, include assistance from family, fellowships, scholarships, etc.)
• (You’ve mentioned issues or obstacles you had because of your background.) What could have helped?  Or, who or what did? Can you think of fortuitous moments in your career?
• Do you have any regrets about the path you took?
• Is there anything else you would like to add? Anything that the Taskforce should take note of, that we did not ask you about here?

Further Readings

Britten, Nicky. 1995. “Qualitative Interviews in Medical Research.” BMJ: British Medical Journal 31(6999):251–253. A good basic overview of interviewing particularly useful for students of public health and medical research generally.

Corbin, Juliet, and Janice M. Morse. 2003. “The Unstructured Interactive Interview: Issues of Reciprocity and Risks When Dealing with Sensitive Topics.” Qualitative Inquiry 9(3):335–354. Weighs the potential benefits and harms of conducting interviews on topics that may cause emotional distress. Argues that the researcher’s skills and code of ethics should ensure that the interviewing process provides more of a benefit to both participant and researcher than a harm to the former.

Gerson, Kathleen, and Sarah Damaske. 2020. The Science and Art of Interviewing . New York: Oxford University Press. A useful guidebook/textbook for both undergraduates and graduate students, written by sociologists.

Kvale, Steiner. 2007. Doing Interviews . London: SAGE. An easy-to-follow guide to conducting and analyzing interviews by psychologists.

Lamont, Michèle, and Ann Swidler. 2014. “Methodological Pluralism and the Possibilities and Limits of Interviewing.” Qualitative Sociology 37(2):153–171. Written as a response to various debates surrounding the relative value of interview-based studies and ethnographic studies defending the particular strengths of interviewing. This is a must-read article for anyone seriously engaging in qualitative research!

Pugh, Allison J. 2013. “What Good Are Interviews for Thinking about Culture? Demystifying Interpretive Analysis.” American Journal of Cultural Sociology 1(1):42–68. Another defense of interviewing written against those who champion ethnographic methods as superior, particularly in the area of studying culture. A classic.

Rapley, Timothy John. 2001. “The ‘Artfulness’ of Open-Ended Interviewing: Some considerations in analyzing interviews.” Qualitative Research 1(3):303–323. Argues for the importance of “local context” of data production (the relationship built between interviewer and interviewee, for example) in properly analyzing interview data.

Weiss, Robert S. 1995. Learning from Strangers: The Art and Method of Qualitative Interview Studies . New York: Simon and Schuster. A classic and well-regarded textbook on interviewing. Because Weiss has extensive experience conducting surveys, he contrasts the qualitative interview with the survey questionnaire well; particularly useful for those trained in the latter.

• I say “normally” because how people understand their various identities can itself be an expansive topic of inquiry. Here, I am merely talking about collecting otherwise unexamined demographic data, similar to how we ask people to check boxes on surveys. ↵
• Again, this applies to “semistructured in-depth interviewing.” When conducting standardized questionnaires, you will want to ask each question exactly as written, without deviations! ↵
• I always include “INT” in the number because I sometimes have other kinds of data with their own numbering: FG#001 would mean the first focus group, for example. I also always include three-digit spaces, as this allows for up to 999 interviews (or, more realistically, allows for me to interview up to one hundred persons without having to reset my numbering system). ↵

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

A document listing key questions and question areas for use during an interview.  It is used most often for semi-structured interviews.  A good interview guide may have no more than ten primary questions for two hours of interviewing, but these ten questions will be supplemented by probes and relevant follow-ups throughout the interview.  Most IRBs require the inclusion of the interview guide in applications for review.  See also interview and  semi-structured interview .

A data-collection method that relies on casual, conversational, and informal interviewing.  Despite its apparent conversational nature, the researcher usually has a set of particular questions or question areas in mind but allows the interview to unfold spontaneously.  This is a common data-collection technique among ethnographers.  Compare to the semi-structured or in-depth interview .

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Follow-up questions used in a semi-structured interview  to elicit further elaboration.  Suggested prompts can be included in the interview guide  to be used/deployed depending on how the initial question was answered or if the topic of the prompt does not emerge spontaneously.

A form of interview that follows a strict set of questions, asked in a particular order, for all interview subjects.  The questions are also the kind that elicits short answers, and the data is more “informative” than probing.  This is often used in mixed-methods studies, accompanying a survey instrument.  Because there is no room for nuance or the exploration of meaning in structured interviews, qualitative researchers tend to employ semi-structured interviews instead.  See also interview.

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

An interview variant in which a person’s life story is elicited in a narrative form.  Turning points and key themes are established by the researcher and used as data points for further analysis.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

What is Qualitative in Qualitative Research

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• Published: 27 February 2019
• Volume 42 , pages 139–160, ( 2019 )

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What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

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If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, 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. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

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Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

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Aspers, P., Corte, U. What is Qualitative in Qualitative Research. Qual Sociol 42 , 139–160 (2019). https://doi.org/10.1007/s11133-019-9413-7

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Research Design Review

A discussion of qualitative & quantitative research design, qualitative research “participants” are not “respondents” (& other misplaced concepts from quantitative research).

But to assume that there is a direct relationship between qualitative and quantitative research would be a grave mistake. As discussed in an article posted in 2013 – “10 Distinctive Qualities of Qualitative Research” – the design, implementation, analysis, and interpretation of qualitative research make it unique and uniquely suited to go beyond survey research to study the complexities and meaning of the human experience.

And yet, researchers – both qualitative and quantitative – regularly overextend the applicability of quantitative ideas to qualitative research design. Although survey research informs the researcher of the basic elements of “good research” – and draws the researcher’s attention to core criteria dealing with sampling, error, bias, and so on – many quantitative concepts and techniques cannot and should not be considered in qualitative research. Here are just three examples:

Generalization. It may seem obvious to most researchers that the limited and highly variable nature of qualitative research makes it a poor predictor of things to come; however, many researchers have advocated the “generalizability” of qualitative data. Whether to further a budding theory or make assertions about an entire population segment, the concept of generalization in the context of qualitative research comes up often. In referring to the case study method, for instance, Earl Babbie, in his seventh edition of The Basics of Social Research (2016), laments “the limited generalizability of what is observed in a single instance of some phenomenon,” stating further that “this risk is reduced, however, when more than one case is studied in depth” (p. 312).

Qualitative research does not need generalization to be valuable but it does need transferability – i.e., the ability to transfer the qualitative design and/or outcomes to other highly specific contexts. Transferability is discussed in several Research Design Review articles, including this one posted in 2013 .

Percentages & data graphs. Qualitative researchers have been known to use percentages to report various aspects of their findings (Smith, 2011). There is also a tendency to use graphs or charts of some sort to display the data. Illustrations can be useful to help visualize qualitative data but there is no reason why the researcher needs to fall back on bar graphs or pie charts. Even when no percentages are used – e.g., the histograms of tagged content made available by online discussion platforms – the appearance of a quantitative-like data display not only hints that the researcher believes the qualitative data are quantifiable but also serves to ignore the whole point of qualitative research – i.e., the analysis of context and personal meaning – by reducing the data to a graphical configuration.

“Respondent.” The survey respondent is appropriately referred to as a “respondent” because that is exactly the role they play in the research process. They are responding to the researcher’s questions which are typically structured and closed-ended in format. Similarly, the qualitative research participant is suitably labeled “participant” because their role goes beyond simply replying to a series of questions to encompass participation in the research on many levels. The participant elaborates on the interviewer’s/moderator’s questions, changes the topic if need be to convey an idea, takes part in a social relationship with the interviewer/moderator, engages with other participants in a focus group discussion, is willingly observed in an ethnographic study, and, in some instances, is asked to aid in the analysis. For all of these reasons (and more), it is research participants that provide qualitative data not respondents.

Babbie, E. R. (2016). The basics of social research (7th ed.). Cengage learning.

Smith, K. (2011). Anxiety, Knowledge and Help: A Model for How Black and White College Students Search for HIV/AIDS Information on the Internet. The Qualitative Report , 16 (1), 103-125. Retrieved from https://nsuworks.nova.edu/tqr/vol16/iss1/6

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Good write up

Neither qualitative nor quantitative participants should be called respondents! They are people.

I find the use of graphs and percentages can provide a mixed method contribution, which Bridges the two approaches along a continuum rather than perceiving them as black and white. I do appreciate many of the points you have raised and have shared your article with my network. Worthy of discourse for sure.

Agreed, that is the beauty of the social sciences!

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Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis

Albine moser.

a Faculty of Health Care, Research Centre Autonomy and Participation of Chronically Ill People , Zuyd University of Applied Sciences , Heerlen, The Netherlands

b Faculty of Health, Medicine and Life Sciences, Department of Family Medicine , Maastricht University , Maastricht, The Netherlands

Irene Korstjens

c Faculty of Health Care, Research Centre for Midwifery Science , Zuyd University of Applied Sciences , Maastricht, The Netherlands

In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By ‘novice’ we mean Master’s students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.

Key points on sampling, data collection and analysis

• The data collection plan needs to be broadly defined and open during data collection.
• Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used.
• Data saturation determines sample size and is different for each study.
• The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions.
• Analyses of ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory or a descriptive summary, respectively.

Introduction

This article is the third paper in a series of four articles aiming to provide practical guidance to qualitative research. In an introductory paper, we have described the objective, nature and outline of the Series [ 1 ]. Part 2 of the series focused on context, research questions and design of qualitative research [ 2 ]. In this paper, Part 3, we address frequently asked questions (FAQs) about sampling, data collection and analysis.

What is a sampling plan?

A sampling plan is a formal plan specifying a sampling method, a sample size, and procedure for recruiting participants ( Box 1 ) [ 3 ]. A qualitative sampling plan describes how many observations, interviews, focus-group discussions or cases are needed to ensure that the findings will contribute rich data. In quantitative studies, the sampling plan, including sample size, is determined in detail in beforehand but qualitative research projects start with a broadly defined sampling plan. This plan enables you to include a variety of settings and situations and a variety of participants, including negative cases or extreme cases to obtain rich data. The key features of a qualitative sampling plan are as follows. First, participants are always sampled deliberately. Second, sample size differs for each study and is small. Third, the sample will emerge during the study: based on further questions raised in the process of data collection and analysis, inclusion and exclusion criteria might be altered, or the sampling sites might be changed. Finally, the sample is determined by conceptual requirements and not primarily by representativeness. You, therefore, need to provide a description of and rationale for your choices in the sampling plan. The sampling plan is appropriate when the selected participants and settings are sufficient to provide the information needed for a full understanding of the phenomenon under study.

Sampling strategies in qualitative research. Based on Polit & Beck [ 3 ].

Some practicalities: a critical first step is to select settings and situations where you have access to potential participants. Subsequently, the best strategy to apply is to recruit participants who can provide the richest information. Such participants have to be knowledgeable on the phenomenon and can articulate and reflect, and are motivated to communicate at length and in depth with you. Finally, you should review the sampling plan regularly and adapt when necessary.

What sampling strategies can I use?

Sampling is the process of selecting or searching for situations, context and/or participants who provide rich data of the phenomenon of interest [ 3 ]. In qualitative research, you sample deliberately, not at random. The most commonly used deliberate sampling strategies are purposive sampling, criterion sampling, theoretical sampling, convenience sampling and snowball sampling. Occasionally, the ‘maximum variation,’ ‘typical cases’ and ‘confirming and disconfirming’ sampling strategies are used. Key informants need to be carefully chosen. Key informants hold special and expert knowledge about the phenomenon to be studied and are willing to share information and insights with you as the researcher [ 3 ]. They also help to gain access to participants, especially when groups are studied. In addition, as researcher, you can validate your ideas and perceptions with those of the key informants.

What is the connection between sampling types and qualitative designs?

The ‘big three’ approaches of ethnography, phenomenology, and grounded theory use different types of sampling.

In ethnography, the main strategy is purposive sampling of a variety of key informants, who are most knowledgeable about a culture and are able and willing to act as representatives in revealing and interpreting the culture. For example, an ethnographic study on the cultural influences of communication in maternity care will recruit key informants from among a variety of parents-to-be, midwives and obstetricians in midwifery care practices and hospitals.

Phenomenology uses criterion sampling, in which participants meet predefined criteria. The most prominent criterion is the participant’s experience with the phenomenon under study. The researchers look for participants who have shared an experience, but vary in characteristics and in their individual experiences. For example, a phenomenological study on the lived experiences of pregnant women with psychosocial support from primary care midwives will recruit pregnant women varying in age, parity and educational level in primary midwifery practices.

Grounded theory usually starts with purposive sampling and later uses theoretical sampling to select participants who can best contribute to the developing theory. As theory construction takes place concurrently with data collection and analyses, the theoretical sampling of new participants also occurs along with the emerging theoretical concepts. For example, one grounded theory study tested several theoretical constructs to build a theory on autonomy in diabetes patients [ 4 ]. In developing the theory, the researchers started by purposefully sampling participants with diabetes differing in age, onset of diabetes and social roles, for example, employees, housewives, and retired people. After the first analysis, researchers continued with theoretically sampling, for example, participants who differed in the treatment they received, with different degrees of care dependency, and participants who receive care from a general practitioner (GP), at a hospital or from a specialist nurse, etc.

In addition to the ‘big three’ approaches, content analysis is frequently applied in primary care research, and very often uses purposive, convenience, or snowball sampling. For instance, a study on peoples’ choice of a hospital for elective orthopaedic surgery used snowball sampling [ 5 ]. One elderly person in the private network of one researcher personally approached potential respondents in her social network by means of personal invitations (including letters). In turn, respondents were asked to pass on the invitation to other eligible candidates.

Sampling is also dependent on the characteristics of the setting, e.g., access, time, vulnerability of participants, and different types of stakeholders. The setting, where sampling is carried out, is described in detail to provide thick description of the context, thereby, enabling the reader to make a transferability judgement (see Part 3: transferability). Sampling also affects the data analysis, where you continue decision-making about whom or what situations to sample next. This is based on what you consider as still missing to get the necessary information for rich findings (see Part 1: emergent design). Another point of attention is the sampling of ‘invisible groups’ or vulnerable people. Sampling of these participants would require applying multiple sampling strategies, and more time calculated in the project planning stage for sampling and recruitment [ 6 ].

How do sample size and data saturation interact?

A guiding principle in qualitative research is to sample only until data saturation has been achieved. Data saturation means the collection of qualitative data to the point where a sense of closure is attained because new data yield redundant information [ 3 ].

Data saturation is reached when no new analytical information arises anymore, and the study provides maximum information on the phenomenon. In quantitative research, by contrast, the sample size is determined by a power calculation. The usually small sample size in qualitative research depends on the information richness of the data, the variety of participants (or other units), the broadness of the research question and the phenomenon, the data collection method (e.g., individual or group interviews) and the type of sampling strategy. Mostly, you and your research team will jointly decide when data saturation has been reached, and hence whether the sampling can be ended and the sample size is sufficient. The most important criterion is the availability of enough in-depth data showing the patterns, categories and variety of the phenomenon under study. You review the analysis, findings, and the quality of the participant quotes you have collected, and then decide whether sampling might be ended because of data saturation. In many cases, you will choose to carry out two or three more observations or interviews or an additional focus group discussion to confirm that data saturation has been reached.

When designing a qualitative sampling plan, we (the authors) work with estimates. We estimate that ethnographic research should require 25–50 interviews and observations, including about four-to-six focus group discussions, while phenomenological studies require fewer than 10 interviews, grounded theory studies 20–30 interviews and content analysis 15–20 interviews or three-to-four focus group discussions. However, these numbers are very tentative and should be very carefully considered before using them. Furthermore, qualitative designs do not always mean small sample numbers. Bigger sample sizes might occur, for example, in content analysis, employing rapid qualitative approaches, and in large or longitudinal qualitative studies.

Data collection

What methods of data collection are appropriate.

The most frequently used data collection methods are participant observation, interviews, and focus group discussions. Participant observation is a method of data collection through the participation in and observation of a group or individuals over an extended period of time [ 3 ]. Interviews are another data collection method in which an interviewer asks the respondents questions [ 6 ], face-to-face, by telephone or online. The qualitative research interview seeks to describe the meanings of central themes in the life world of the participants. The main task in interviewing is to understand the meaning of what participants say [ 5 ]. Focus group discussions are a data collection method with a small group of people to discuss a given topic, usually guided by a moderator using a questioning-route [ 8 ]. It is common in qualitative research to combine more than one data collection method in one study. You should always choose your data collection method wisely. Data collection in qualitative research is unstructured and flexible. You often make decisions on data collection while engaging in fieldwork, the guiding questions being with whom, what, when, where and how. The most basic or ‘light’ version of qualitative data collection is that of open questions in surveys. Box 2 provides an overview of the ‘big three’ qualitative approaches and their most commonly used data collection methods.

Qualitative data collection methods.

What role should I adopt when conducting participant observations?

What is important is to immerse yourself in the research setting, to enable you to study it from the inside. There are four types of researcher involvement in observations, and in your qualitative study, you may apply all four. In the first type, as ‘complete participant’, you become part of the setting and play an insider role, just as you do in your own work setting. This role might be appropriate when studying persons who are difficult to access. The second type is ‘active participation’. You have gained access to a particular setting and observed the group under study. You can move around at will and can observe in detail and depth and in different situations. The third role is ‘moderate participation’. You do not actually work in the setting you wish to study but are located there as a researcher. You might adopt this role when you are not affiliated to the care setting you wish to study. The fourth role is that of the ‘complete observer’, in which you merely observe (bystander role) and do not participate in the setting at all. However, you cannot perform any observations without access to the care setting. Such access might be easily obtained when you collect data by observations in your own primary care setting. In some cases, you might observe other care settings, which are relevant to primary care, for instance observing the discharge procedure for vulnerable elderly people from hospital to primary care.

How do I perform observations?

It is important to decide what to focus on in each individual observation. The focus of observations is important because you can never observe everything, and you can only observe each situation once. Your focus might differ between observations. Each observation should provide you with answers regarding ‘Who do you observe?’, ‘What do you observe’, ‘Where does the observation take place?’, ‘When does it take place?’, ‘How does it happen?’, and ‘Why does it happen as it happens?’ Observations are not static but proceed in three stages: descriptive, focused, and selective. Descriptive means that you observe, on the basis of general questions, everything that goes on in the setting. Focused observation means that you observe certain situations for some time, with some areas becoming more prominent. Selective means that you observe highly specific issues only. For example, if you want to observe the discharge procedure for vulnerable elderly people from hospitals to general practice, you might begin with broad observations to get to know the general procedure. This might involve observing several different patient situations. You might find that the involvement of primary care nurses deserves special attention, so you might then focus on the roles of hospital staff and primary care nurses, and their interactions. Finally, you might want to observe only the specific situations where hospital staff and primary care nurses exchange information. You take field notes from all these observations and add your own reflections on the situations you observed. You jot down words, whole sentences or parts of situations, and your reflections on a piece of paper. After the observations, the field notes need to be worked out and transcribed immediately to be able to include detailed descriptions.

Further reading on interviews and focus group discussion.

Qualitative data analysis.

What are the general features of an interview?

Interviews involve interactions between the interviewer(s) and the respondent(s) based on interview questions. Individual, or face-to-face, interviews should be distinguished from focus group discussions. The interview questions are written down in an interview guide [ 7 ] for individual interviews or a questioning route [ 8 ] for focus group discussions, with questions focusing on the phenomenon under study. The sequence of the questions is pre-determined. In individual interviews, the sequence depends on the respondents and how the interviews unfold. During the interview, as the conversation evolves, you go back and forth through the sequence of questions. It should be a dialogue, not a strict question–answer interview. In a focus group discussion, the sequence is intended to facilitate the interaction between the participants, and you might adapt the sequence depending on how their discussion evolves. Working with an interview guide or questioning route enables you to collect information on specific topics from all participants. You are in control in the sense that you give direction to the interview, while the participants are in control of their answers. However, you need to be open-minded to recognize that some relevant topics for participants may not have been covered in your interview guide or questioning route, and need to be added. During the data collection process, you develop the interview guide or questioning route further and revise it based on the analysis.

The interview guide and questioning route might include open and general as well as subordinate or detailed questions, probes and prompts. Probes are exploratory questions, for example, ‘Can you tell me more about this?’ or ‘Then what happened?’ Prompts are words and signs to encourage participants to tell more. Examples of stimulating prompts are eye contact, leaning forward and open body language.

Further reading on qualitative analysis.

What is a face-to-face interview?

A face-to-face interview is an individual interview, that is, a conversation between participant and interviewer. Interviews can focus on past or present situations, and on personal issues. Most qualitative studies start with open interviews to get a broad ‘picture’ of what is going on. You should not provide a great deal of guidance and avoid influencing the answers to fit ‘your’ point of view, as you want to obtain the participant’s own experiences, perceptions, thoughts, and feelings. You should encourage the participants to speak freely. As the interview evolves, your subsequent major and subordinate questions become more focused. A face-to-face or individual interview might last between 30 and 90 min.

Most interviews are semi-structured [ 3 ]. To prepare an interview guide to enhance that a set of topics will be covered by every participant, you might use a framework for constructing a semi-structured interview guide [ 10 ]: (1) identify the prerequisites to use a semi-structured interview and evaluate if a semi-structured interview is the appropriate data collection method; (2) retrieve and utilize previous knowledge to gain a comprehensive and adequate understanding of the phenomenon under study; (3) formulate a preliminary interview guide by operationalizing the previous knowledge; (4) pilot-test the preliminary interview guide to confirm the coverage and relevance of the content and to identify the need for reformulation of questions; (5) complete the interview guide to collect rich data with a clear and logical guide.

The first few minutes of an interview are decisive. The participant wants to feel at ease before sharing his or her experiences. In a semi-structured interview, you would start with open questions related to the topic, which invite the participant to talk freely. The questions aim to encourage participants to tell their personal experiences, including feelings and emotions and often focus on a particular experience or specific events. As you want to get as much detail as possible, you also ask follow-up questions or encourage telling more details by using probes and prompts or keeping a short period of silence [ 6 ]. You first ask what and why questions and then how questions.

You need to be prepared for handling problems you might encounter, such as gaining access, dealing with multiple formal and informal gatekeepers, negotiating space and privacy for recording data, socially desirable answers from participants, reluctance of participants to tell their story, deciding on the appropriate role (emotional involvement), and exiting from fieldwork prematurely.

What is a focus group discussion and when can I use it?

A focus group discussion is a way to gather together people to discuss a specific topic of interest. The people participating in the focus group discussion share certain characteristics, e.g., professional background, or share similar experiences, e.g., having diabetes. You use their interaction to collect the information you need on a particular topic. To what depth of information the discussion goes depends on the extent to which focus group participants can stimulate each other in discussing and sharing their views and experiences. Focus group participants respond to you and to each other. Focus group discussions are often used to explore patients’ experiences of their condition and interactions with health professionals, to evaluate programmes and treatment, to gain an understanding of health professionals’ roles and identities, to examine the perception of professional education, or to obtain perspectives on primary care issues. A focus group discussion usually lasts 90–120 mins.

You might use guidelines for developing a questioning route [ 9 ]: (1) brainstorm about possible topics you want to cover; (2) sequence the questioning: arrange general questions first, and then, more specific questions, and ask positive questions before negative questions; (3) phrase the questions: use open-ended questions, ask participants to think back and reflect on their personal experiences, avoid asking ‘why’ questions, keep questions simple and make your questions sound conversational, be careful about giving examples; (4) estimate the time for each question and consider: the complexity of the question, the category of the question, level of participant’s expertise, the size of the focus group discussion, and the amount of discussion you want related to the question; (5) obtain feedback from others (peers); (6) revise the questions based on the feedback; and (7) test the questions by doing a mock focus group discussion. All questions need to provide an answer to the phenomenon under study.

You need to be prepared to manage difficulties as they arise, for example, dominant participants during the discussion, little or no interaction and discussion between participants, participants who have difficulties sharing their real feelings about sensitive topics with others, and participants who behave differently when they are observed.

How should I compose a focus group and how many participants are needed?

The purpose of the focus group discussion determines the composition. Smaller groups might be more suitable for complex (and sometimes controversial) topics. Also, smaller focus groups give the participants more time to voice their views and provide more detailed information, while participants in larger focus groups might generate greater variety of information. In composing a smaller or larger focus group, you need to ensure that the participants are likely to have different viewpoints that stimulate the discussion. For example, if you want to discuss the management of obesity in a primary care district, you might want to have a group composed of professionals who work with these patients but also have a variety of backgrounds, e.g. GPs, community nurses, practice nurses in general practice, school nurses, midwives or dieticians.

Focus groups generally consist of 6–12 participants. Careful time management is important, since you have to determine how much time you want to devote to answering each question, and how much time is available for each individual participant. For example, if you have planned a focus group discussion lasting 90 min. with eight participants, you might need 15 min. for the introduction and the concluding summary. This means you have 75 min. for asking questions, and if you have four questions, this allows a total of 18 min. of speaking time for each question. If all eight respondents participate in the discussion, this boils down to about two minutes of speaking time per respondent per question.

How can I use new media to collect qualitative data?

New media are increasingly used for collecting qualitative data, for example, through online observations, online interviews and focus group discussions, and in analysis of online sources. Data can be collected synchronously or asynchronously, with text messaging, video conferences, video calls or immersive virtual worlds or games, etcetera. Qualitative research moves from ‘virtual’ to ‘digital’. Virtual means those approaches that import traditional data collection methods into the online environment and digital means those approaches take advantage of the unique characteristics and capabilities of the Internet for research [ 10 ]. New media can also be applied. See Box 3 for further reading on interview and focus group discussion.

Can I wait with my analysis until all data have been collected?

You cannot wait with the analysis, because an iterative approach and emerging design are at the heart of qualitative research. This involves a process whereby you move back and forth between sampling, data collection and data analysis to accumulate rich data and interesting findings. The principle is that what emerges from data analysis will shape subsequent sampling decisions. Immediately after the very first observation, interview or focus group discussion, you have to start the analysis and prepare your field notes.

Why is a good transcript so important?

First, transcripts of audiotaped interviews and focus group discussions and your field notes constitute your major data sources. Trained and well-instructed transcribers preferably make transcripts. Usually, e.g., in ethnography, phenomenology, grounded theory, and content analysis, data are transcribed verbatim, which means that recordings are fully typed out, and the transcripts are accurate and reflect the interview or focus group discussion experience. Most important aspects of transcribing are the focus on the participants’ words, transcribing all parts of the audiotape, and carefully revisiting the tape and rereading the transcript. In conversation analysis non-verbal actions such as coughing, the lengths of pausing and emphasizing, tone of voice need to be described in detail using a formal transcription system (best known are G. Jefferson’s symbols).

To facilitate analysis, it is essential that you ensure and check that transcripts are accurate and reflect the totality of the interview, including pauses, punctuation and non-verbal data. To be able to make sense of qualitative data, you need to immerse yourself in the data and ‘live’ the data. In this process of incubation, you search the transcripts for meaning and essential patterns, and you try to collect legitimate and insightful findings. You familiarize yourself with the data by reading and rereading transcripts carefully and conscientiously, in search for deeper understanding.

Are there differences between the analyses in ethnography, phenomenology, grounded theory, and content analysis?

Ethnography, phenomenology, and grounded theory each have different analytical approaches, and you should be aware that each of these approaches has different schools of thought, which may also have integrated the analytical methods from other schools ( Box 4 ). When you opt for a particular approach, it is best to use a handbook describing its analytical methods, as it is better to use one approach consistently than to ‘mix up’ different schools.

In general, qualitative analysis begins with organizing data. Large amounts of data need to be stored in smaller and manageable units, which can be retrieved and reviewed easily. To obtain a sense of the whole, analysis starts with reading and rereading the data, looking at themes, emotions and the unexpected, taking into account the overall picture. You immerse yourself in the data. The most widely used procedure is to develop an inductive coding scheme based on actual data [ 11 ]. This is a process of open coding, creating categories and abstraction. In most cases, you do not start with a predefined coding scheme. You describe what is going on in the data. You ask yourself, what is this? What does it stand for? What else is like this? What is this distinct from? Based on this close examination of what emerges from the data you make as many labels as needed. Then, you make a coding sheet, in which you collect the labels and, based on your interpretation, cluster them in preliminary categories. The next step is to order similar or dissimilar categories into broader higher order categories. Each category is named using content-characteristic words. Then, you use abstraction by formulating a general description of the phenomenon under study: subcategories with similar events and information are grouped together as categories and categories are grouped as main categories. During the analysis process, you identify ‘missing analytical information’ and you continue data collection. You reread, recode, re-analyse and re-collect data until your findings provide breadth and depth.

Throughout the qualitative study, you reflect on what you see or do not see in the data. It is common to write ‘analytic memos’ [ 3 ], write-ups or mini-analyses about what you think you are learning during the course of your study, from designing to publishing. They can be a few sentences or pages, whatever is needed to reflect upon: open codes, categories, concepts, and patterns that might be emerging in the data. Memos can contain summaries of major findings and comments and reflections on particular aspects.

In ethnography, analysis begins from the moment that the researcher sets foot in the field. The analysis involves continually looking for patterns in the behaviours and thoughts of the participants in everyday life, in order to obtain an understanding of the culture under study. When comparing one pattern with another and analysing many patterns simultaneously, you may use maps, flow charts, organizational charts and matrices to illustrate the comparisons graphically. The outcome of an ethnographic study is a narrative description of a culture.

In phenomenology, analysis aims to describe and interpret the meaning of an experience, often by identifying essential subordinate and major themes. You search for common themes featuring within an interview and across interviews, sometimes involving the study participants or other experts in the analysis process. The outcome of a phenomenological study is a detailed description of themes that capture the essential meaning of a ‘lived’ experience.

Grounded theory generates a theory that explains how a basic social problem that emerged from the data is processed in a social setting. Grounded theory uses the ‘constant comparison’ method, which involves comparing elements that are present in one data source (e.g., an interview) with elements in another source, to identify commonalities. The steps in the analysis are known as open, axial and selective coding. Throughout the analysis, you document your ideas about the data in methodological and theoretical memos. The outcome of a grounded theory study is a theory.

Descriptive generic qualitative research is defined as research designed to produce a low inference description of a phenomenon [ 12 ]. Although Sandelowski maintains that all research involves interpretation, she has also suggested that qualitative description attempts to minimize inferences made in order to remain ‘closer’ to the original data [ 12 ]. Descriptive generic qualitative research often applies content analysis. Descriptive content analysis studies are not based on a specific qualitative tradition and are varied in their methods of analysis. The analysis of the content aims to identify themes, and patterns within and among these themes. An inductive content analysis [ 11 ] involves breaking down the data into smaller units, coding and naming the units according to the content they present, and grouping the coded material based on shared concepts. They can be represented by clustering in treelike diagrams. A deductive content analysis [ 11 ] uses a theory, theoretical framework or conceptual model to analyse the data by operationalizing them in a coding matrix. An inductive content analysis might use several techniques from grounded theory, such as open and axial coding and constant comparison. However, note that your findings are merely a summary of categories, not a grounded theory.

Analysis software can support you to manage your data, for example by helping to store, annotate and retrieve texts, to locate words, phrases and segments of data, to name and label, to sort and organize, to identify data units, to prepare diagrams and to extract quotes. Still, as a researcher you would do the analytical work by looking at what is in the data, and making decisions about assigning codes, and identifying categories, concepts and patterns. The computer assisted qualitative data analysis (CAQDAS) website provides support to make informed choices between analytical software and courses: http://www.surrey.ac.uk/sociology/research/researchcentres/caqdas/support/choosing . See Box 5 for further reading on qualitative analysis.

The next and final article in this series, Part 4, will focus on trustworthiness and publishing qualitative research [ 13 ].

Acknowledgements

The authors thank the following junior researchers who have been participating for the last few years in the so-called ‘Think tank on qualitative research’ project, a collaborative project between Zuyd University of Applied Sciences and Maastricht University, for their pertinent questions: Erica Baarends, Jerome van Dongen, Jolanda Friesen-Storms, Steffy Lenzen, Ankie Hoefnagels, Barbara Piskur, Claudia van Putten-Gamel, Wilma Savelberg, Steffy Stans, and Anita Stevens. The authors are grateful to Isabel van Helmond, Joyce Molenaar and Darcy Ummels for proofreading our manuscripts and providing valuable feedback from the ‘novice perspective’.

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.