qualitative observations experiment

Chapter 13. Participant Observation

Introduction.

Although there are many possible forms of data collection in the qualitative researcher’s toolkit, the two predominant forms are interviewing and observing. This chapter and the following chapter explore observational data collection. While most observers also include interviewing, many interviewers do not also include observation. It takes some special skills and a certain confidence to be a successful observer. There is also a rich tradition of what I am going to call “deep ethnography” that will be covered in chapter 14. In this chapter, we tackle the basics of observational data collection.

What is Participant Observation?

While interviewing helps us understand how people make sense of their worlds, observing them helps us understand how they act and behave. Sometimes, these actions and behaviors belie what people think or say about their beliefs and values and practices. For example, a person can tell you they would never racially discriminate, but observing how they actually interact with racialized others might undercut those statements. This is not always about dishonesty. Most of us tend to act differently than we think we do or think we should. That is part of being human. If you are interested in what people say and believe , interviewing is a useful technique for data collection. If you are interested in how people act and behave , observing them is essential. And if you want to know both, particularly how thinking/believing and acting/behaving complement or contradict each other, then a combination of interviewing and observing is ideal.

There are a variety of terms we use for observational data collection, from ethnography to fieldwork to participant observation . Many researchers use these terms fairly interchangeably, but here I will separately define them. The subject of this chapter is observation in general, or participant observation, to highlight the fact that observers can also be participants. The subject of chapter 14 will be deep ethnography , a particularly immersive form of study that is attractive for a certain subset of qualitative researchers. Both participant observation and deep ethnography are forms of fieldwork in which the researcher leaves their office and goes into a natural setting to record observations that take place in that setting. [1]

Participant observation (PO) is a field approach to gathering data in which the researcher enters a specific site for purposes of engagement or observation. Participation and observation can be conceptualized as a continuum, and any given study can fall somewhere on that line between full participation (researcher is a member of the community or organization being studied) and observation (researcher pretends to be a fly on the wall surreptitiously but mostly by permission, recording what happens). Participant observation forms the heart of ethnographic research, an approach, if you remember, that seeks to understand and write about a particular culture or subculture. We’ll discuss what I am calling deep ethnography in the next chapter, where researchers often embed themselves for months if not years or even decades with a particular group to be able to fully capture “what it’s like.” But there are lighter versions of PO that can form the basis of a research study or that can supplement or work with other forms of data collection, such as interviews or archival research. This chapter will focus on these lighter versions, although note that much of what is said here can also apply to deep ethnography (chapter 14).

PO methods of gathering data present some special considerations—How involved is the researcher? How close is she to the subjects or site being studied? And how might her own social location—identity, position—affect the study? These are actually great questions for any kind of qualitative data collection but particularly apt when the researcher “enters the field,” so to speak. It is helpful to visualize where one falls on a continuum or series of continua (figure 13.1).

Let’s take a few examples and see how these continua work. Think about each of the following scenarios, and map them onto the possibilities of figure 13.1:

• a nursing student during COVID doing research on patient/doctor interactions in the ICU
• a graduate student accompanying a police officer during her rounds one day in a part of the city the graduate student has never visited
• a professor raised Amish who goes back to her hometown to conduct research on Amish marriage practices for one month
•  (What if the sociologist was also a member of the OCF board and camping crew?)

Depending on how the researcher answers those questions and where they stand on the P.O. continuum, various techniques will be more or less effective. For example, in cases where the researcher is a participant, writing reflective fieldnotes at the end of the day may be the primary form of data collected. After all, if the researcher is fully participating, they probably don’t have the time or ability to pull out a notepad and ask people questions. On the other side, when a researcher is more of an observer, this is exactly what they might do, so long as the people they are interrogating are able to answer while they are going about their business. The more an observer, the more likely the researcher will engage in relatively structured interviews (using techniques discussed in chapters 11 and 12); the more a participant, the more likely casual conversations or “unstructured interviews” will form the core of the data collected. [2]

Observation and Qualitative Traditions

Observational techniques are used whenever the researcher wants to document actual behaviors and practices as they happen (not as they are explained or recorded historically). Many traditions of inquiry employ observational data collection, but not all traditions employ them in the same way. Chapter 14 will cover one very specific tradition: ethnography. Because the word ethnography is sometimes used for all fieldwork, I am calling the subject of chapter 14 deep ethnography, those studies that take as their focus the documentation through the description of a culture or subculture. Deeply immersive, this tradition of ethnography typically entails several months or even years in the field. But there are plenty of other uses of observation that are less burdensome to the researcher.

Grounded Theory, in which theories emerge from a rigorous and systematic process of induction, is amenable to both interviewing and observing forms of data collection, and some of the best Grounded Theory works employ a deft combination of both. Often closely aligned with Grounded Theory in sociology is the tradition of symbolic interactionism (SI). Interviews and observations in combination are necessary to properly address the SI question, What common understandings give meaning to people’s interactions ? Gary Alan Fine’s body of work fruitfully combines interviews and observations to build theory in response to this SI question. His Authors of the Storm: Meteorologists and the Culture of Prediction is based on field observation and interviews at the Storm Prediction Center in Oklahoma; the National Weather Service in Washington, DC; and a few regional weather forecasting outlets in the Midwest. Using what he heard and what he observed, he builds a theory of weather forecasting based on social and cultural factors that take place inside local offices. In Morel Tales: The Culture of Mushrooming , Fine investigates the world of mushroom hunters through participant observation and interviews, eventually building a theory of “naturework” to describe how the meanings people hold about the world are constructed and are socially organized—our understanding of “nature” is based on human nature, if you will.

Phenomenology typically foregrounds interviewing, as the purpose of this tradition is to gather people’s understandings and meanings about a phenomenon. However, it is quite common for phenomenological interviewing to be supplemented with some observational data, especially as a check on the “reality” of the situations being described by those interviewed. In my own work, for example, I supplemented primary interviews with working-class college students with some participant observational work on the campus in which they were studying. This helped me gather information on the general silence about class on campus, which made the salience of class in the interviews even more striking ( Hurst 2010a ).

Critical theories such as standpoint approaches, feminist theory, and Critical Race Theory are often multimethod in design. Interviews, observations (possibly participation), and archival/historical data are all employed to gather an understanding of how a group of persons experiences a particular setting or institution or phenomenon and how things can be made more just . In Making Elite Lawyers , Robert Granfield ( 1992 ) drew on both classroom observations and in-depth interviews with students to document the conservatizing effects of the Harvard legal education on working-class students, female students, and students of color. In this case, stories recounted by students were amplified by searing examples of discrimination and bias observed by Granfield and reported in full detail through his fieldnotes.

Entry Access and Issues

Managing your entry into a field site is one of the most important and nerve-wracking aspects of doing ethnographic research. Unlike interviews, which can be conducted in neutral settings, the field is an actual place with its own rules and customs that you are seeking to explore. How you “gain access” will depend on what kind of field you are entering. If your field site is a physical location with walls and a front desk (such as an office building or an elementary school), you will need permission from someone in the organization to enter and to conduct your study. Negotiating this might take weeks or even months. If your field site is a public site (such as a public dog park or city sidewalks), there is no “official” gatekeeper, but you will still probably need to find a person present at the site who can vouch for you (e.g., other dog owners or people hanging out on their stoops). [3] And if your field site is semipublic, as in a shopping mall, you might have to weigh the pros and cons of gaining “official” permission, as this might impede your progress or be difficult to ascertain whose permission to request. If you recall, many of the ethical dilemmas discussed in chapter 7 were about just such issues.

Even with official (or unofficial) permission to enter the site, however, your quest to gain access is not done. You will still need to gain the trust and permission of the people you encounter at that site. If you are a mere observer in a public setting, you probably do not need each person you observe to sign a consent form, but if you are a participant in an event or enterprise who is also taking notes and asking people questions, you probably do. Each study is unique here, so I recommend talking through the ethics of permission and consent seeking with a faculty mentor.

A separate but related issue from permission is how you will introduce yourself and your presence. How you introduce yourself to people in the field will depend very much on what level of participation you have chosen as well as whether you are an insider or outsider. Sometimes your presence will go unremarked, whereas other times you may stick out like a very sore thumb. Lareau ( 2021 ) advises that you be “vague but accurate” when explaining your presence. You don’t want to use academic jargon (unless your field is the academy!) that would be off-putting to the people you meet. Nor do you want to deceive anyone. “Hi, I’m Allison, and I am here to observe how students use career services” is accurate and simple and more effective than “I am here to study how race, class, and gender affect college students’ interactions with career services personnel.”

Researcher Note

Something that surprised me and that I still think about a lot is how to explain to respondents what I’m doing and why and how to help them feel comfortable with field work. When I was planning fieldwork for my dissertation, I was thinking of it from a researcher’s perspective and not from a respondent’s perspective. It wasn’t until I got into the field that I started to realize what a strange thing I was planning to spend my time on and asking others to allow me to do. Like, can I follow you around and write notes? This varied a bit by site—it was easier to ask to sit in on meetings, for example—but asking people to let me spend a lot of time with them was awkward for me and for them. I ended up asking if I could shadow them, a verb that seemed to make clear what I hoped to be able to do. But even this didn’t get around issues like respondents’ self-consciousness or my own. For example, respondents sometimes told me that their lives were “boring” and that they felt embarrassed to have someone else shadow them when they weren’t “doing anything.” Similarly, I would feel uncomfortable in social settings where I knew only one person. Taking field notes is not something to do at a party, and when introduced as a researcher, people would sometimes ask, “So are you researching me right now?” The answer to that is always yes. I figured out ways of taking notes that worked (I often sent myself text messages with jotted notes) and how to get more comfortable explaining what I wanted to be able to do (wanting to see the campus from the respondent’s perspective, for example), but it is still something I work to improve.

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

Reflexivity in Fieldwork

As always, being aware of who you are, how you are likely to be read by others in the field, and how your own experiences and understandings of the world are likely to affect your reading of others in the field are all very important to conducting successful research. When Annette Lareau ( 2021 ) was managing a team of graduate student researchers in her study of parents and children, she noticed that her middle-class graduate students took in stride the fact that children called adults by their first names, while her working-class-origin graduate students “were shocked by what they considered the rudeness and disrespect middle-class children showed toward their parents and other adults” ( 151 ). This “finding” emerged from particular fieldnotes taken by particular research assistants. Having graduate students with different class backgrounds turned out to be useful. Being reflexive in this case meant interrogating one’s own expectations about how children should act toward adults. Creating thick descriptions in the fieldnotes (e.g., describing how children name adults) is important, but thinking about one’s response to those descriptions is equally so. Without reflection, it is possible that important aspects never even make it into the fieldnotes because they seem “unremarkable.”

The Data of Observational Work: Fieldnotes

In interview data collection, recordings of interviews are transcribed into the data of the study. This is not possible for much PO work because (1) aural recordings of observations aren’t possible and (2) conversations that take place on-site are not easily recorded. Instead, the participant observer takes notes, either during the fieldwork or at the day’s end. These notes, called “fieldnotes,” are then the primary form of data for PO work.

Writing fieldnotes takes a lot of time. Because fieldnotes are your primary form of data, you cannot be stingy with the time it takes. Most practitioners suggest it takes at least the same amount of time to write up notes as it takes to be in the field, and many suggest it takes double the time. If you spend three hours at a meeting of the organization you are observing, it is a good idea to set aside five to six hours to write out your fieldnotes. Different researchers use different strategies about how and when to do this. Somewhat obviously, the earlier you can write down your notes, the more likely they are to be accurate. Writing them down at the end of the day is thus the default practice. However, if you are plainly exhausted, spending several hours trying to recall important details may be counterproductive. Writing fieldnotes the next morning, when you are refreshed and alert, may work better.

Reseaarcher Note

How do you take fieldnotes ? Any advice for those wanting to conduct an ethnographic study?

Fieldnotes are so important, especially for qualitative researchers. A little advice when considering how you approach fieldnotes: Record as much as possible! Sometimes I write down fieldnotes, and I often audio-record them as well to transcribe later. Sometimes the space to speak what I observed is helpful and allows me to be able to go a little more in-depth or to talk out something that I might not quite have the words for just yet. Within my fieldnote, I include feelings and think about the following questions: How do I feel before data collection? How did I feel when I was engaging/watching? How do I feel after data collection? What was going on for me before this particular data collection? What did I notice about how folks were engaging? How were participants feeling, and how do I know this? Is there anything that seems different than other data collections? What might be going on in the world that might be impacting the participants? As a qualitative researcher, it’s also important to remember our own influences on the research—our feelings or current world news may impact how we observe or what we might capture in fieldnotes.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

What should be included in those fieldnotes? The obvious answer is “everything you observed and heard relevant to your research question.” The difficulty is that you often don’t know what is relevant to your research question when you begin, as your research question itself can develop and transform during the course of your observations. For example, let us say you begin a study of second-grade classrooms with the idea that you will observe gender dynamics between both teacher and students and students and students. But after five weeks of observation, you realize you are taking a lot of notes about how teachers validate certain attention-seeking behaviors among some students while ignoring those of others. For example, when Daisy (White female) interrupts a discussion on frogs to tell everyone she has a frog named Ribbit, the teacher smiles and asks her to tell the students what Ribbit is like. In contrast, when Solomon (Black male) interrupts a discussion on the planets to tell everyone his big brother is called Jupiter by their stepfather, the teacher frowns and shushes him. These notes spark interest in how teachers favor and develop some students over others and the role of gender, race, and class in these teacher practices. You then begin to be much more careful in recording these observations, and you are a little less attentive to the gender dynamics among students. But note that had you not been fairly thorough in the first place, these crucial insights about teacher favoritism might never have been made.

Here are some suggestions for things to include in your fieldnotes as you begin: (1) descriptions of the physical setting; (2) people in the site: who they are and how they interact with one another (what roles they are taking on); and (3) things overheard: conversations, exchanges, questions. While you should develop your own personal system for organizing these fieldnotes (computer vs. printed journal, for example), at a minimum, each set of fieldnotes should include the date, time in the field, persons observed, and location specifics. You might also add keywords to each set so that you can search by names of participants, dates, and locations. Lareau ( 2021:167 ) recommends covering the following key issues, which mnemonically spell out WRITE— W : who, what, when, where, how; R: reaction (responses to the action in question and the response to the response); I: inaction (silence or nonverbal response to an action); T: timing (how slowly or quickly someone is speaking); and E: emotions (nonverbal signs of emotion and/or stoicism).

In addition to the observational fieldnotes, if you have time, it is a good practice to write reflective memos in which you ask yourself what you have learned (either about the study or about your abilities in the field). If you don’t have time to do this for every set of fieldnotes, at least get in the practice of memoing at certain key junctures, perhaps after reading through a certain number of fieldnotes (e.g., every third day of fieldnotes, you set aside two hours to read through the notes and memo). These memos can then be appended to relevant fieldnotes. You will be grateful for them when it comes time to analyze your data, as they are a preliminary by-the-seat-of-your-pants analysis. They also help steer you toward the study you want to pursue rather than allow you to wallow in unfocused data.

Ethics of Fieldwork

Because most fieldwork requires multiple and intense interactions (even if merely observational) with real living people as they go about their business, there are potentially more ethical choices to be made. In addition to the ethics of gaining entry and permission discussed above, there are issues of accurate representation, of respecting privacy, of adequate financial compensation, and sometimes of financial and other forms of assistance (when observing/interacting with low-income persons or other marginalized populations). In other words, the ethical decision of fieldwork is never concluded by obtaining a signature on a consent form. Read this brief selection from Pascale’s ( 2021 ) methods description (observation plus interviews) to see how many ethical decisions she made:

Throughout I kept detailed ethnographic field and interview records, which included written notes, recorded notes, and photographs. I asked everyone who was willing to sit for a formal interview to speak only for themselves and offered each of them a prepaid Visa Card worth $25–40. I also offered everyone the opportunity to keep the card and erase the tape completely at any time they were dissatisfied with the interview in any way. No one asked for the tape to be erased; rather, people remarked on the interview being a really good experience because they felt heard. Each interview was professionally transcribed and for the most part the excerpts in this book are literal transcriptions. In a few places, the excerpta have been edited to reduce colloquial features of speech (e.g., you know, like, um) and some recursive elements common to spoken language. A few excerpts were placed into standard English for clarity. I made this choice for the benefit of readers who might otherwise find the insights and ideas harder to parse in the original. However, I have to acknowledge this as an act of class-based violence. I tried to keep the original phrasing whenever possible. ( 235 )

Summary Checklist for Successful Participant Observation

The following are ten suggestions for being successful in the field, slightly paraphrased from Patton ( 2002:331 ). Here, I take those ten suggestions and turn them into an extended “checklist” to use when designing and conducting fieldwork.

• Consider all possible approaches to your field and your position relative to that field (see figure 13.2). Choose wisely and purposely. If you have access to a particular site or are part of a particular culture, consider the advantages (and disadvantages) of pursuing research in that area. Clarify the amount of disclosure you are willing to share with those you are observing, and justify that decision.
• Take thorough and descriptive field notes. Consider how you will record them. Where your research is located will affect what kinds of field notes you can take and when, but do not fail to write them! Commit to a regular recording time. Your field notes will probably be the primary data source you collect, so your study’s success will depend on thick descriptions and analytical memos you write to yourself about what you are observing.
• Permit yourself to be flexible. Consider alternative lines of inquiry as you proceed. You might enter the field expecting to find something only to have your attention grabbed by something else entirely. This is perfectly fine (and, in some traditions, absolutely crucial for excellent results). When you do see your attention shift to an emerging new focus, take a step back, look at your original research design, and make careful decisions about what might need revising to adapt to these new circumstances.
• Include triangulated data as a means of checking your observations. If you are that ICU nurse watching patient/doctor interactions, you might want to add a few interviews with patients to verify your interpretation of the interaction. Or perhaps pull some public data on the number of arrests for jaywalking if you are the student accompanying police on their rounds to find out if the large number of arrests you witnessed was typical.
• Respect the people you are witnessing and recording, and allow them to speak for themselves whenever possible. Using direct quotes (recorded in your field notes or as supplementary recorded interviews) is another way to check the validity of the analyses of your observations. When designing your research, think about how you can ensure the voices of those you are interested in get included.
•  Choose your informants wisely. Who are they relative to the field you are exploring? What are the limitations (ethical and strategic) in using those particular informants, guides, and gatekeepers? Limit your reliance on them to the extent possible.
• Consider all the stages of fieldwork, and have appropriate plans for each. Recognize that different talents are required at different stages of the data-collection process. In the beginning, you will probably spend a great deal of time building trust and rapport and will have less time to focus on what is actually occurring. That’s normal. Later, however, you will want to be more focused on and disciplined in collecting data while also still attending to maintaining relationships necessary for your study’s success. Sometimes, especially when you have been invited to the site, those granting access to you will ask for feedback. Be strategic about when giving that feedback is appropriate. Consider how to extricate yourself from the site and the participants when your study is coming to an end. Have an ethical exit plan.
• Allow yourself to be immersed in the scene you are observing. This is true even if you are observing a site as an outsider just one time. Make an effort to see things through the eyes of the participants while at the same time maintaining an analytical stance. This is a tricky balance to do, of course, and is more of an art than a science. Practice it. Read about how others have achieved it.
• Create a practice of separating your descriptive notes from your analytical observations. This may be as clear as dividing a sheet of paper into two columns, one for description only and the other for questions or interpretation (as we saw in chapter 11 on interviewing), or it may mean separating out the time you dedicate to descriptions from the time you reread and think deeply about those detailed descriptions. However you decide to do it, recognize that these are two separate activities, both of which are essential to your study’s success.
• As always with qualitative research, be reflective and reflexive. Do not forget how your own experience and social location may affect both your interpretation of what you observe and the very things you observe themselves (e.g., where a patient says more forgiving things about an observably rude doctor because they read you, a nursing student, as likely to report any negative comments back to the doctor). Keep a research journal!

Further Readings

Emerson, Robert M., Rachel I. Fretz, and Linda L. Shaw. 2011. Writing Ethnographic Fieldnotes . 2nd ed. University of Chicago Press. Excellent guide that uses actual unfinished fieldnote to illustrate various options for composing, reviewing, and incorporating fieldnote into publications.

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up . Chicago: University of Chicago Press. Includes actual fieldnote from various studies with a really helpful accompanying discussion about how to improve them!

Wolfinger, Nicholas H. 2002. “On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2(1):85–95. Uses fieldnote from various sources to show how the researcher’s expectations and preexisting knowledge affect what gets written about; offers strategies for taking useful fieldnote.

• Note that leaving one’s office to interview someone in a coffee shop would not be considered fieldwork because the coffee shop is not an element of the study. If one sat down in a coffee shop and recorded observations, then this would be fieldwork. ↵
• This is one reason why I have chosen to discuss deep ethnography in a separate chapter (chapter 14). ↵
• This person is sometimes referred to as the [pb_glossary id="389"]informant [/pb_glossary](and more on these characters in chapter 14). ↵

Methodological tradition of inquiry that holds the view that all social interaction is dependent on shared views of the world and each other, characterized through people’s use of language and non-verbal communication.   Through interactions, society comes to be.  The goal of the researcher in this tradition is to trace that construction, as in the case of documenting how gender is “done” or performed, demonstrating the fluidity of the concept (and how it is constantly being made and remade through daily interactions).

Used primarily in ethnography , as in the goal of fieldnotes is to produce a thick description of what is both observed directly (actions, actors, setting, etc.) and the meanings and interpretations being made by those actors at the time.  In this way, the observed cultural and social relationships are contextualized for future interpretation.  The opposite of a thick description is a thin description, in which observations are recorded without any social context or cues to help explain them.  The term was coined by anthropologist Clifford Geertz (see chapter 14 ).

Reflective summaries of findings that emerge during analysis of qualitative data; they can include reminders to oneself for future analyses or considerations, reinterpretations or generations of codes, or brainstorms and concept mapping.

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.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalizability

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

• Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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What is Qualitative Observation?

Observations in research methods

Uses for qualitative observations, what are qualitative observation examples, qualitative observation characteristics, what are the types of qualitative observation, observation data in qualitative research, considerations for observational research.

Let's learn about the method of qualitative observations and the types of observations, then look at some helpful examples.

• Uses for qualitative observation

Observation exists as a research method that collects data based on what is observed or experienced.

While data from an experiment is structured and assumes a form that provides for easy data analysis , observation data can adopt as many forms as can be perceived by the researcher.

As a result, the data can be text, audio, images, or even video , all of which can be subject to data analysis through qualitative or quantitative methods .

What is the difference between qualitative and quantitative observations?

There are qualitative and quantitative observations. A quantitative observation can involve measurements of the numerical value of observed phenomena (e.g., the size of a crowd or the weight of an object). The quantitative data collected from such an observation might be helpful for a statistical research study in contexts where the theory is already established and needs confirmation or critique.

On the other hand, a qualitative observation adopts a naturalistic inquiry to understand a phenomenon whose attributes may not be quantified. Qualitative researchers might be more interested in capturing data about the physical appearance of certain people, the sounds of a particular event, or the feel of a specific texture in building materials.

To understand qualitative observation, let's list some contexts where qualitative researchers conduct observational research .

• client management
• market research
• health research
• classroom pedagogy

This method deals with many different topics, including:

• sleep patterns of long-term patients
• personal contact between individuals
• animal behavior in the presence of humans
• reproductive behavior of endangered species
• environmental conditions in a species' natural habitat

Direct observation is ideal for these sorts of inquiries, as experimental or quantitative research cannot capture the sort of rich data found in a natural environment.

This means that qualitative observation often requires more than textual description . For example, when a researcher wants to compare how supporters of a particular sports team might interact with other fans, they may want to document different styles of clothing or the sounds and images associated with each sports team.

In such cases, the researcher will want to employ a data collection method that documents video, audio, and images for later discussion. An especially illustrative picture collected for this study can provide a helpful example to research audiences of the culture being discussed.

Here are several examples to consider:

• observing how people walk through a busy train station during rush hour
• observing how students in a cooking class learn how to make a dish
• observing how new mothers hold their newborns while recovering
• observing how people at a zoo get closer to and interact with the animals
• observing how managers at a company train and give feedback to new employees

Often, a textual description of each example may only give you basic insights about each experience.

However, imagine what sensory information you might need to fully understand the experiences in each example in this list.

Consider the following sentence:

"The people in the train station walked to the track leading to their destination."

This provides some surface details, but is it deep enough for readers to feel the experience?

The researcher's role in this case is to observe these contexts and, later on, immerse the research audience with the sensory data from those situations:

• The crowd got louder as they rushed for the train.
• The smell of butter was in the air as students kneaded the bread.
• The mother said her new daughter's skin was smooth.
• The children laughed when the penguins splashed them with cold water.
• The manager wore a dark suit, while his employees wore white coats.

In contrast with typical experimental research, observing has several important traits to consider.

• the researcher is the main source of data
• there are no wrong answers, as readers rely on what the researcher senses
• people may differ on what the sensory information might mean

There is no one qualitative observation definition as it can take on many forms. Generally, there are no right or wrong answers regarding how to observe.

While the different types of qualitative observation may differ in how the researcher engages participants, the process in which they gather data for sensory information is largely the same.

Naturalistic observation

This type of research study involves direct observation of a natural environment and having the researcher document what they see, either in field notes or recorded media .

A researcher can observe from afar and take notes about the sensory information they receive.

This is the simplest method that a researcher can employ. People may also see this method as the most objective as it removes the researcher from the environment altogether.

Participant observation

This kind of qualitative observation relies on the researchers gathering information through participation and reporting on their personal experiences within a given context.

Unlike naturalistic observation, where the researcher can be a complete observer, active participation can collect information about a cultural process or ritual whose essence can only be understood firsthand.

In a complete participant observation, researchers can also ask direct questions to those they observe to gather their perspectives about the process in which they participate. This allows observers to view a culture's characteristics from different angles, rather than rely on one subjective view alone.

Structured observation

In a structured qualitative observation, the researcher isolates their research participants to elicit and observe a certain set of behavioral responses.

For example, a researcher may provide children with some toys in a room to see how they will respond.

Structured observation may also be useful for quantitative observation, especially if the research inquiry relies on observing quantifiable phenomena that might be easier to capture in a controlled setting. Either way, observing people in this manner may not capture the natural part of interactions and behaviors that might exist in a natural environment.

Longitudinal observation

All qualitative observation can be time-consuming, but longitudinal observation can involve the researcher in several weeks or months of study. Examples of qualitative observation that are necessarily longitudinal include studies of academic performance over time, quality of life in palliative care, and impacts of climate change on communities.

These inquiries not only identify examples of phenomena in discrete moments but also across long periods. Researchers conducting qualitative observation over a longitudinal scale should be prepared to observe changes in participants that are otherwise invisible at any particular moment in a study.

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Qualitative data from an observation relies on the researcher's sensory organs to describe the context of the study .

What do researchers see? What do they hear or smell?

While this may not necessarily sound scientific, qualitative observation can provide many an example of the characteristics of sensory phenomena that may not be available in a basic, textual description of research.

ATLAS.ti is especially useful for analysis of qualitative observation data . Qualitative data can take on many different formats from plain text to video files to PDF documents. This is especially important when the qualitative observation method relies on the five senses for qualitative data collection .

In Document Manager, researchers can create projects in ATLAS.ti to store all of their various project-related files for easy and efficient analysis later.

Qualitative observations have particular characteristics that set them apart from other qualitative or experimental methods.

Researcher subjectivity

One of the most common critiques of qualitative observation is that it generates subjective data , given that subjective methods are employed to gather data and can be limited by personal bias . When discussing qualitative observations, the researcher should take care to acknowledge and express their own biases to their research audiences to establish transparency in the research study and data they present.

Readers of observation research benefit from understanding how the researcher views the context they observe, the attributes of the phenomenon they see, and the thought process they adopt to describe the findings in their study. This requires providing the audience with a clear definition of the phenomenon they want to describe as well as a detailed accounting of what the researcher assumes about the phenomenon.

Theoretical development

Typically, data analysis of qualitative observation is based on inductive reasoning aimed at developing theories for completely unknown or largely unknown phenomena. This is different from quantitative observation or experimental research that seeks to confirm existing theory and whose methods seek to identify trends or provide the right or wrong answer to a research inquiry.

Theoretical development places equal importance to both qualitative research and quantitative research. However, the theory generated from qualitative observation can be strengthened through quantitative or experimental research by conducting further qualitative observation in another context to mitigate the subjective nature or any individual study.

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Qualitative Observation – Definition with Examples

Qualitative observation is a valuable research method that allows researchers to delve into the complexities of human experiences, gather data, and gain insights into the subjective aspects of a given phenomenon. Qualitative observation primarily focuses on understanding the observed behavior or event’s meaning, context, and nuances. It involves a systematic and detailed examination of phenomena to obtain subjective data and explore the depth of human experiences.

LEARN ABOUT: Behavioral Research

Unlike quantitative methods that rely on numerical measurements, qualitative observation seeks to capture qualitative data, which refers to non-numerical information such as thoughts, emotions, perceptions, and social interactions. When conducting qualitative observation, researchers employ various techniques to collect data. These techniques may include participant qualitative observation, interviews, focus groups, and document analysis.

LEARN ABOUT: Qualitative Interview

What is Qualitative Observation?

Qualitative Observation is the research process of using subjective methodologies to gather data or data. Since the focus on qualitative observation is to equate quality differences, it is a lot more time consuming than quantitative observation but the sample size used is much smaller and the research is extensive and a lot more personal.

The subjective nature of qualitative observation acknowledges that the researcher’s interpretations and biases shape the data collection and analysis process. It emphasizes the importance of understanding the social and cultural context, the participants’ perspectives, and the researcher’s reflexivity. Qualitative observation deals with the 5 major sensory organs and their functioning – sight, smell, touch, taste and hearing. This doesn’t involve measurements or numbers but instead characteristics.

Learn more: Qualitative Market Research

 Qualitative Observation Characteristics for Researcher

Data collection in qualitative observation often involves immersing oneself in the research setting, carefully observing and documenting the participants’ behaviors, actions, and conversations. Characteristics of qualitative observational research can very broadly be bucketed under ten overlapping themes that researchers should know of when they analyze the qualitative data that has been collected. They are:

Inductive content analysis

This characteristic is a major part of qualitative observational research because the interviewer or the researchers immerses himself/herself with the group and gets in sync with the topic. The questions evolve during the research process. The researcher can form any hypothesis through the answers and work backwards to prove or disprove it or even build on it. Another component of this is the researcher evaluates a lot of content which is known as inductive content analysis. This analysis to is used to form hypothesis and act as a primary content type. This approach allows for the findings to emerge from raw data without the restraints of structured methodologies of significant, dominant or repetitive themes.

For example, when someone borrows a book from you. They say they will return it in 2 weeks but don’t. And then do that a few more times. Every time a date is decided on, that is a premise. But if the book isn’t returned after a few such instances, you assume that you are never getting the book back. That is the conclusion.

Personal contact and insight

The researcher has to be aware of the “Halo effect” during a research study. Whilst it is important to immerse yourself with the subjects for a study, it is also counter-productive to form a bias. Being emotionally vested in a study helps to derive better answers but it is also a slippery slope if the researcher lets the topic get research biased .

A good example for this would be an influencer being the researcher for a sports shoe manufacturer’s study with current and prospective customers. The researcher can offer important inputs toward the research but offering personal suggestions or product tweaks would bias the study and the corresponding research.

Naturalism or naturalistic inquiry

This type of qualitative observation and qualitative research is the type of research that focuses on how people react or behave when they are put in a real life situation in a natural environment. This characteristic hinges on the reality that things in general are coherent, consistent and predictable. Hence the researcher here would try every extent to control the contours of the environment the study is happening in so that the study happens in context.

For example if you wanted to understand from students how many of them use e-learning modules, you cannot do this in a cafeteria where all the students may not be taking online courses. It would have to be done in online forums or through video conferencing.

Dynamic systems

Qualitative observational research focuses on getting multiple answers. There’s no right or wrong answer and hence the researcher must prod for every possible aspect towards the study. It is also imperative that the researcher motivates the participants to provide every variant of the answer that they think is right.

An example would be in a sample research with a few participants to discuss a new mobile phone features, the researcher should push the respondents to talk about every feature they think is important or not or add something that is still only on the drawing board.

Qualitative holistic perspective 

This approach assumes that the whole is greater than the sum of all the parts. This means that every action or communication in a research study has to be accounted for as part of their culture or community. But. But if not careful, the researcher assumes every little thing to be relevant and that leds the researcher down the wrong path with qualitative observation.

A very good example of this is the use of plastic bags in a certain country. If a lot of the people are interviewed about their plastic usage and discuss how to reduce the usage, the usage would never reduce.

Unique case orientation study

Researchers must never lose focus of the fact that each study is different from another and equal importance and time and emotions must be devoted to each research. Researchers must also realize no matter what outcome of a study is required, the same amount of time has to be devoted to the research.

An example of this is a focus group on the color of a clothing item is as important as the focus group on the design, fabric and fit.

Context sensitivity

The researcher must be sensitive to the qualitative observation fact that different ethnographics respond to the same question very differently and he/she should not negate an opinion or thought on the basis of a personal bias. They must also realize that certain demographics, geographical locations or even cultural behavior can influence the variables for each question. The researchers should be able to account for them and see patterns and map them in the analysis.

Focus groups with various people of different ethnicities being asked about their food preferences is an example of this characteristic. People of different religions and different geographies respond to different ways to food because of their upbringing, the nutritional value of the food, religious beliefs etc.

Empathetic neutrality

Ideally, researchers should be non-judgmental while compiling findings of a research study. But being completely neutral is not possible for a human being, this concept is a controversial topic in steps in qualitative research .

For example, an orthopedic surgeon who was the researcher for a study cannot be biased towards orthopedic doctors who were respondents of the study whilst putting down the other medical professionals.

Qualitative data research

Many qualitative methods like interviews, samples and research reports can help triangulate the cultural orientation of a group in a research study. This is summation of the culture the way it is. A researcher can do the ground research work to find a common bond and then conduct the actual interviews to get their point of view – this is qualitative data .

Learn more: Qualitative Data Collection

For example, trying to understand why Eastern African runners do well in long distance competitive running. Reports show you the results and the researchers go into a study with that premise and then conduct actual interviews to understand the reasons behind their dominance.

Design flexibility

Researchers can deep dive into certain threads that come out of a research study even though it may not be directly relevant to the central theme of the study. This is to coerce the recipients of the study to answer being fully invested in the study of qualitative observation.

This can be denoted with if a restaurant is coming up with a new venue and the central theme is Mexican food but after the research, there seems to be some interest for South American food too. The qualitative researchers should take cognizance of the request and build on it.

To summarize, it is paramount that the researcher has an open mind to the study and can distance himself/herself from any bias or a halo effect. The researcher must also be aware of their own biases and know how to keep those biases away whilst representing a group.

Learn more: Qualitative Research Questions and Questionnaires

Different Types of Qualitative Observations

Even though qualitative observation is subjective, the qualitative researchers must define the end result and quantify it so that the research is actionable. The researcher must also be aware of bias and try to not let that engulf the research. It also helps to have more than one researcher so that the accumulated research is holistic in nature. The four types of qualitative observations are:

Complete observer

In this type of qualitative observation, the researcher is completely unknown to the research audience and cannot even be seen. This type of research gives the audience more freedom to speak because they think they are not being observed or judged. But this method of qualitative observation is losing ground over other types because of privacy issues. In today’s day and world, one cannot observe you without your knowledge.

This model although is the only option in a public place like a lounge, restaurant or a coffee shop. The other alternative to this is to have a camera recording the focus group or discussion that the group is having.

Observer as participant

In this type of qualitative observation, the researcher is known to the focus group or the people in the sample undergoing the study. In this study type, the end goal of the researcher is known to everyone. In this case the observer can play an active part in the discussion. But it is preferred if the suggestions given are limited so that it doesn’t influence the research outcome or sway the group towards a certain bias.

An example of this type of study is when a fan of a certain football club is doing an extensive research if the club of his/her choice is going to make it to the SuperBowl and if yes or not, what are the reasons for the it with other fans. Other fans know him/her as a fan but not in the capacity of a researcher and hence indulge in the study.

Participant as observer

In this type of qualitative observation, the observer completely indulges the participants and participates in the discussion. Even though the participants discuss in entirety with the observer, they do know that the observer is also a researcher. The observer in this case though is a family member or a close friend and hence that doesn’t deter the participants from a discussion.

An example of this study type is a medical study on an in-depth but a slightly embarrassing topic where the researcher could be related the participant or participants in any way.

Complete participant

This research type is used for secretive topics or research areas that you wouldn’t want to ruffle feathers with. In this case the researcher is completely in sync with the participants. The discussions are free flowing no holds barred and the researcher indulges in the discussion animatedly. In this research type the participants don’t know the researcher or even that a research study is being conducted.

A shopping mall trying to understand purchasing and spend habits of the shoppers is an example of this type of study. This is where the researcher is planted in an already group of participants and the researcher can plant thoughts or ideas or coerce participants to speak up.

Learn more: Conduct Qualitative Research

Examples of Qualitative Observations

To better understand qualitative observation, below are 2 examples:

1. Qualitative observation is called intensive. An example is – A vacation rental owner wanted to understand why there were diminishing guest visits, very few repeat guests and negligible referrals. An online community of the vacation rental home were interviewed to understand their holiday and stay habits and preferences. At the end of the interview, it was realized that the reason for non-repeat visits and no referrals was that the home didn’t contain a washer dryer, it was far from downtown and getting necessities was tough and the home wasn’t pet friendly.

LEARN ABOUT:   Action Research

By conducting this qualitative research in qualitative observation, the owner realized the shortcomings of the place and what were the innate feelings of the guests. Through this research, the homeowner can now rectify or alleviate some of the problem areas.

2. Another example of this is a an investment firm where the objective of the study was to understand the investment trends of customer orientations  with specific fund managers. Some fund managers even though had better results and impressive portfolios were the least picked or had very high retention amongst clients. An online sample of the investment firm was called upon for in-depth focus groups. After days of qualitative data collection and analysis, it was found out that the fund managers who were really good at portfolio management were terrible in client management. They did not explain the funds to their customers or ignored recommendations of their customers on other funds. They also chose not to communicate with their direct customers and maintained stoic silence for the review process.

By conducting this qualitative observation research, the fund manager realized that if his staff was trained better, he could bring up the performance of the laggards and train the good performers in smart research skills to increase client satisfaction.

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Difference between qualitative observation and quantitative observation

There are many differences between qualitative observation and quantitative observation but some of the major differences are:

• Qualitative observation is objective but quantitative observation is subjective.
• Qualitative observation can be conducted with a small sample but in quantitative observation the number is much higher.
• The sample in qualitative observation is counted as the actual but in quantitative observation, a subset can signify the emotions of a larger audience.
• Qualitative observation portrays an individual opinion but quantitative observation is a collection of opinions.

Qualitative observations involve directly observing and studying research participants’ behaviors, actions, and experiences, making it a valuable research method. By focusing on the richness of human experiences, qualitative observations provide in-depth insights that contribute to a holistic understanding of the research topic.

Explore Insightfully Contextual Inquiry in Qualitative Research

Qualitative observation provides a practical approach for researchers to understand better and explore various phenomena. These qualitative methods allow researchers to delve into the depths of human experience, gathering subjective data beyond numerical measurements. Researchers can enhance their overall research process and generate meaningful findings by employing qualitative observation.

The approach facilitates a more comprehensive exploration of human experiences and adds depth to the available information. QuestionPro empowers researchers and organizations to effectively incorporate qualitative observation into their studies, enhancing the depth and richness of their qualitative data. Whether used for market research, academic studies, or employee engagement surveys, QuestionPro is an invaluable resource for unlocking the power of qualitative observation, enabling researchers to gain a deeper understanding and make informed decisions based on comprehensive data.

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Observations

Observation is a research method that enables researchers to systematically observe and record people’s behaviour, actions and interactions. ( Hennink, Hutter, & Bailey, 2020, p.170)

Interested in learning about people’s actions, behaviours and their social interactions in real-time? Want to know more about what influences how people behave and act? Curious to understand more about the context and the environment in which people live? If so, you could consider doing an observational study.

Observational studies have long been used by researchers to:

• observe people’s actions and behaviours first-hand. This can help to put things into perspective and explore how things people say they do actually unfold in practice ( Merriam & Tisdell, 2016 ).
• describe specific places or social settings in detail and learn how people interact with their social, built and natural environments.
• gain insight into the relations within and between groups and communities ( Mulhall , 2002).
• find out about cultural norms and activities within the community ( Hennink, Hutter, & Bailey, 2020).

Getting started

Starting your observation can be challenging. What should you focus on in terms of the context? What behaviours, actions and interactions are relevant to record for your study?

To illustrate how to find your way into an observation, let’s use a concrete example based on an article by Srivarathan and colleagues (2020) called, “Social relations, community engagement and potentials: A qualitative study exploring resident engagement in a community-based health promotion intervention in a deprived social housing area.”

The aim of the research was to explore how people living in a deprived housing area in Copenhagen perceived engaging in a community-based health promotion intervention that focussed on enhancing social relations. The intervention was co-produced with participants and consisted of social outings and visits to historical landmarks in Denmark.

To learn how participants perceived and engaged with the intervention, the researchers asked:

• What motives do residents have for engaging in the interventions, and what do they perceive to be the outcome of their engagement?
• What barriers and what potential for improvement are identified concerning resident engagement in the interventions?

To address these questions, the researchers observed how residents engaged with their environment during the social outings and visits to historical landmarks. Their observations were helped by a team guide consisting of the questions listed below:

• Non-human actors and context . What is the time available for interaction? What is the space/location of the interventions?
• Human actors . What groupings, divisions and positions take place? Can you describe the atmosphere? What interactions between residents/researchers/other people are taking place?
• Communication . What is articulated before, during and after the interventions and what is left unspoken (everyday life context, need for activities, social relations)? Who brings up topics during the conversations and how are they responded to?
• Body language . What non-verbal communication and reactions can you observe?

A guide like this is useful when carrying out qualitative observations to create data relevant to the research objectives and questions. You could consider developing a similar guide for your own project to ensure you focus on relevant aspects of the context, behaviours, actions and interactions.

Types of Observation

There are different types of observations. They can range from participant observation to non-participant observation.

Participant observation : Participant observation is when the researcher directly interacts with the participants and the activities they are engaged in ( Mulhall , 2002). Besides ‘observing’, the researcher can also carry out informal conversations and even interviews with participants to gain additional information about their actions and behaviours.

To illustrate this, let’s return to the example above by Srivarathan and colleagues. When carrying out their study, they used participant observation. That is, the researchers actively participated in the social outings and visits to historical landmarks trying to adopt the perspective of their participants. While taking part in the activities, they also talked to the participants about their experiences and took fieldnotes of their observations following the guide outlined above.

Non-participant observation : Non-participant observation is when the research “observes people, activities or events from a distance” ( Hennink, Hutter, & Bailey, 2020, p.185). The researcher does not participate in the situation they are observing.

For example, had Srivarathan and colleagues decided to conduct a non-participatory study to explore people’s perceptions and engagement with the intervention, they would not have taken an active part in the outings. Instead, they would have observed more detached what people were doing and talking about.

The diagram below further highlights the differences between participant observation and non-participant observation by paying attention to how visible the researcher is in the process.

Chapter 3: Participation Observation ( Guest, Namey, & Mitchell , 2012)

Recording data

All observations require a method to record data. There are different ways you can go about this:

• Written notes, often referred to as field notes, can include text, pictures, diagrams and other illustrative materials
• Video recordings of what you are observing
• Photography providing snapshots of particular situations
• Sound recording to capture crucial aspects of the context

(Based on Wragg , 2011; Hennink, Hutter, & Bailey, 2020)

Limitations

It is important to consider the weaknesses of qualitative observations. These include:

• Gaining consent to observe people and communities can be difficult
• Accessing research sites may be challenging. It can involve negotiation and approval from multiple individuals and groups, including professionals.
• Acquiring participants’ trust and buy-in can take a lot of effort and may be time consuming

(Based on Mulhall , 2002)

To learn more about qualitative observations, explore the resources below.

(Author: Mayah Ramachandran)

What is it?

Collecting qualitative data, introduction to social research: Quantitative and qualitative approaches by Keith Punch (2014)

This book explains the different aspects of qualitative research design. Section 8.2 describes observations outlining the different approaches to observations, the practical issues of observations, and how to improve the quality of your observation data.

(Academic reference: Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches . Sage.)

Being a careful observer, qualitative research: A guide to design and implementation (Chapter 6) by Sharan Merriam and Elizabeth Tisdel (2015)

Chapter 6 in Merriam and Tisdel’s book, Being a Careful Observer, highlights the importance of observation within qualitative research. It outlines what to observe, how to observe and gives practical considerations. It also describes the overlapping relationship between the observer and the participant. The chapter additionally explains how to write field notes, including an example of field notes written from a study investigating adult education in Korea.

(Academic reference: Merriam, S. B., &Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation . John Wiley & Sons.)

Participant observations, action research methods (Chapter 3) by Gail Zieman and Sheri Klein (2012)

The Participant Observation chapter by Gail Zieman explains how the researcher can have varying levels of interaction and visibility when participating in an observation. Zieman discusses how participant observation can be used to research educational settings and explains the important role of teachers as a participant observer.

Academic reference: (Zieman, G. (2012). Chapter 3-Particapnt observations. In Klein, S. Action, research methods . SAGE Publications)

What is qualitative observation? Definition, types and best practices by Nick Jain (n.d)

This website gives a short summary of qualitative observation. It defines qualitative observation, explains the characteristics and the different types of observation. It features seven examples, each conducting qualitative observation within different research fields. The website concludes with ‘Top 8 Qualitative Observation Best Practices’ to improve validity and reliability of an observation.

(Academic reference: Jain, Nick. (n.d.) What is qualitative observation? Definition, types and best practices. IdeaScale. https://ideascale.com/blog/what-is-qualitative-observation/)

How is it done?

Observations, qualitative research methods (Chapter 9) by Monique Hennink, Inge Hutter, and Ajay Bailey (2020)

Chapter 9 provides a comprehensive guide to qualitative observation. It defines observation and outlines when to conduct them, types of observation and how to record observational data. It includes two examples of non-participant observation; one conducted in the Netherlands investigating burial places and the other observing the activities of women in a hotel in East Africa. The book also explains how to write field notes, field diaries and reports. It concludes with a table summarising the strengths and limitations of observation.

(Academic reference: Hennink, M., Hutter, I., Bailey, B. (2020). Qualitative research method . Sage.)

The ultimate guide to qualitative research – Part 2: Handling qualitative data. Field notes in research by ATLAS.ti (n.d)

If you decide to record observational data in the form of field notes, this website is a short, useful guide. It explains what field notes are, explaining their purpose within qualitative research. The website demonstrates how to write field notes, stating the importance of descriptive observations, reflective notes, methodological notes, sketches and diagrams. It offers suggestions on how to prepare for fieldwork and the considerations for qualitative data analysis.

(Academic reference: ATLAS.ti. (n.d.) The Ultimate guide to qualitative research – Part 2: Handling qualitative data. Field notes in research. https://atlasti.com/guides/qualitative-research-guide-part-2/field-notes)

Method in action

Social relations, community engagement and potentials: A qualitative study exploring resident engagement in a community-based health promotion intervention in a deprived social housing area by Abirami Srivarathan, Rikke Lund, Ulla Christensen, and Maria Kristiansen (2020)

This research study explores the perspectives of residents living in deprived housing in Copenhagen when faced with a community-based health promotion intervention to enhance social relations. Srivarathan and colleagues conducted semi structured interviews alongside participant observations which occurred over six months. They observed residents within the housing area as well as when they attended social outings and visited to historical landmarks in Denmark.

(Academic reference: Srivarathan, A., Lund, R., Christensen, U., & Kristiansen, M. (2020). Social relations, community engagement and potentials: A qualitative study exploring resident engagement in a community-based health promotion intervention in a deprived social housing area.  International Journal of Environmental Research & Public Health ,  17 (7), 2341. https://doi.org/10.3390/ijerph17072341)

“What matters to me”: A multi-method qualitative study exploring service users’, carers’ and clinicians’ needs and experiences of therapeutic engagement on acute mental health wards by Sarah McAllister, Alan Simpson, Vicki Tsianakas and Glenn Robert (2021)

This article used non-participant observation and semi-structured interviews to investigate service users’ and clinicians’ experience of therapeutic engagement in acute hospital wards. A mental health nurse trained in research methods conducted 80-hours observation, recording nurse-patient interactions on a mental health ward. Observation field notes were coded and thematically analysed to understand the nature of the nurse-patient interactions.

(Academic reference: McAllister, S., Simpson, A., Tsianakas, V., & Robert, G. (2021). “What matters to me”: A multi‐method qualitative study exploring service users’, carers’ and clinicians’ needs and experiences of therapeutic engagement on acute mental health wards.  International Journal of Mental Health Nursing ,  30 (3), 703-714.)

A qualitative study in a rural community: Investigating the attitudes, beliefs, and interactions of young children and their parents regarding storybook read alouds by Natalie Conrad Barnyak (2011)

This article is a qualitative research study investigating the attitudes and beliefs about reading aloud held by young rural families in Western Pennsylvania . Families participated in semi-structured interviews and non-participant observation. Barnyak observed family reading sessions via video recordings which were later analysed. The author then compared the semi-structured interviews and the observation data, to understand how the observed interactions aligned with the parent’s self-reported attitudes about reading books with their children.

(Academic reference: Barnyak, N. C. (2011). A Qualitative Study in a Rural Community: Investigating the Attitudes, Beliefs, and Interactions of Young Children and Their Parents Regarding Storybook Read Alouds.  Early Childhood Education Journal ,  39 , 149-159.)

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalisability

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

• Labour-intensive

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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What is Qualitative Observation? Definition, Types, Examples

Appinio Research · 03.05.2024 · 30min read

Have you ever wondered how researchers gain deep insights into human behavior and social interactions? Qualitative observation offers a fascinating window into the complexities of everyday life, allowing researchers to immerse themselves in natural settings and observe people in their element. From bustling city streets to quiet coffee shops, qualitative observation captures the nuances, subtleties, and context-specific dynamics that shape human experiences. In this guide, we'll explore the definition, purpose, methods, and applications of qualitative observation, providing practical insights and tips for conducting meaningful research and understanding the world around us. Whether you're a student delving into the realm of social sciences or a curious individual eager to explore the intricacies of human behavior, this guide will equip you with the knowledge and skills to navigate the fascinating terrain of qualitative observation with confidence and clarity.

What is Qualitative Observation?

Qualitative observation is a research method used to gather detailed insights into human behavior, experiences, and interactions through direct observation in natural settings. Unlike quantitative methods that rely on numerical data and statistical analysis , qualitative observation focuses on capturing the richness, complexity, and context of social phenomena through descriptive and interpretive means.

The primary purpose of qualitative observation is to deepen understanding and generate insights into the subjective experiences, perspectives, and behaviors of individuals and groups within their natural environments. By immersing researchers in the context of the study, qualitative observation facilitates the exploration of social dynamics, cultural norms, and contextual factors that shape human behavior.

Importance of Qualitative Observation in Research

• Richness of Insights : Qualitative observation allows researchers to explore the depth and complexity of human behavior and interactions in real-world settings, providing rich and detailed insights that may not be captured through quantitative methods alone.
• Contextual Understanding : By immersing researchers in the natural environments of participants, qualitative observation enables the exploration of behavior within its social, cultural, and environmental contexts. This contextual understanding is essential for interpreting and making sense of human actions and interactions.
• Theory Development : Qualitative observation contributes to theory development by generating new hypotheses, concepts , and frameworks grounded in empirical evidence and real-world observations. It allows researchers to uncover patterns , themes, and relationships that inform theoretical perspectives and models.
• Informing Practice : In addition to its role in academic research, qualitative observation has practical applications in various fields, including education, healthcare , business, and social services. It informs decision-making, program development, and policy formulation by providing insights into people's needs, preferences , and experiences.
• Enhanced Validity : Qualitative observation enhances the validity and credibility of research findings by complementing quantitative data with rich, contextualized insights. Triangulation of data sources and methods increases the robustness of research conclusions and reduces the risk of bias or misinterpretation.
• Personal Development : Beyond its role in academic and professional contexts, qualitative observation offers opportunities for personal growth and development. Engaging in observation and reflection fosters empathy, cultural competence, and critical thinking skills, enhancing researchers' capacity to navigate diverse social contexts and understand human behavior.

Qualitative observation is not only a valuable research method but also a powerful tool for understanding and navigating the complexities of human behavior and social interactions in everyday life. Its emphasis on context, depth, and interpretation makes it a versatile and indispensable approach in both research and practice.

Understanding Qualitative Observation

Qualitative observation serves as a fundamental research method across various disciplines, providing rich insights into human behavior, social interactions, and cultural dynamics. To effectively utilize qualitative observation, it's crucial to understand its underlying principles and the different types of observation methods available.

Qualitative Observation Principles

Qualitative observation operates on several fundamental principles that shape the approach to data collection , analysis, and interpretation:

• Subjectivity vs. Objectivity : Unlike quantitative methods that aim for objectivity and generalizability, qualitative observation acknowledges the subjective nature of human experiences. Researchers recognize their role as active participants in the research process, influencing the interpretation of data through their perspectives and biases.
• Contextual Understanding : Qualitative observation emphasizes the importance of understanding behavior within its social, cultural, and environmental contexts. By immersing themselves in the natural settings of participants, researchers gain a deeper appreciation for the factors that shape human actions and interactions.
• Holistic Perspective : Qualitative observation adopts a holistic approach to studying phenomena, focusing on the interconnectedness of various elements within a given context. Researchers seek to capture the complexity and nuance of human experiences, considering multiple layers of meaning and interpretation.
• Inductive Reasoning : Qualitative observation often employs inductive reasoning, allowing patterns and themes to emerge from the data rather than imposing preconceived hypotheses or theories. This open-ended approach enables researchers to explore new insights and perspectives that may challenge existing paradigms.

Types of Qualitative Observation

Qualitative observation encompasses a diverse range of methods, each offering unique advantages and considerations for data collection and analysis:

• Participant Observation : In participant observation, researchers immerse themselves in the natural settings of participants, actively engaging in social interactions and activities. By becoming part of the environment under study, researchers gain insider perspectives and access to rich, contextualized data. This method is particularly well-suited for studying cultural practices, group dynamics, and everyday behaviors.
• Naturalistic Observation : Naturalistic observation involves observing people in their natural environments without intervention or manipulation by the researcher. Researchers adopt a passive role, simply observing and documenting behaviors as they naturally occur. This method provides authentic insights into real-world behaviors and interactions, free from artificial constraints or biases.
• Structured Observation : Structured observation involves defining specific behaviors, events, or criteria for observation in advance. Researchers develop structured protocols or checklists to guide data collection, ensuring consistency and reliability across observations. While less flexible than participant and naturalistic observation, this method allows for standardized data collection and comparison across different contexts or groups.

Each type of qualitative observation offers distinct advantages and challenges, and researchers must carefully consider the appropriateness of each method based on their research goals, context, and ethical considerations.

Qualitative observation is an invaluable tool for gaining deep insights into human behavior and social interactions. With Appinio , conducting qualitative research becomes a seamless and efficient process, allowing researchers to collect real-time consumer insights in minutes without the hassle. By leveraging our intuitive platform and global reach, researchers can unlock a wealth of qualitative data to inform their decision-making and drive business success. Say goodbye to lengthy research processes and hello to actionable insights at your fingertips.

Ready to experience the power of Appinio for yourself? Book a demo today and discover how our platform can revolutionize your qualitative research endeavors!

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How to Prepare for Qualitative Observation?

Before embarking on qualitative observation, thorough planning and preparation are essential to ensure the success and ethical integrity of your study.

1. Define Research Objectives

Defining clear and specific research objectives is the cornerstone of any qualitative observation study. Your research objectives serve as guiding principles that shape your study's scope, focus, and direction.

• Identify Research Questions : Start by identifying the key questions you want to address through your qualitative observation. What phenomena are you interested in exploring? What specific aspects of human behavior or social interactions do you want to investigate?
• Clarify Purpose and Scope : Clearly articulate the purpose and scope of your study. What do you hope to achieve through your observation? Are you aiming to generate new insights, test existing theories, or explore a particular phenomenon in depth?
• Consider Practical Constraints : Take into account any practical constraints or limitations that may impact your research objectives, such as time, resources, and access to participants or observation settings. Set realistic goals that align with the available resources and logistical considerations.

2. Select Observation Methods

Once you've defined your research objectives, the next step is to select the most appropriate observation methods to achieve your goals. Qualitative observation offers a variety of techniques, each with its own strengths and considerations.

• Research Objectives : Choose observation methods that align with your research objectives and questions. Consider whether you need to immerse yourself in the environment as a participant, observe behaviors from a distance, or use structured protocols for data collection.
• Context and Setting : Take into account the specific context and setting of your study. Are you observing individuals in a naturalistic environment, such as a public space or workplace, or are you conducting observations within a controlled setting, such as a laboratory or simulated environment?
• Ethical Considerations : Consider the ethical implications of different observation methods, particularly in terms of privacy, consent, and potential risks to participants. Ensure that your chosen methods adhere to ethical guidelines and respect the rights and dignity of participants.

3. Identify Observation Settings

Identifying the appropriate observation settings is crucial for ensuring the validity and relevance of your observations. The observation setting should provide access to the phenomena of interest while allowing for naturalistic and unobtrusive observation.

• Access and Permissions : Obtain necessary permissions and access to the observation settings, whether they are public spaces, private institutions, or community settings. Seek cooperation from relevant stakeholders, such as facility managers, organizational leaders, or community members.
• Naturalistic Environments : Whenever possible, choose observation settings that reflect the natural environments where the phenomena of interest naturally occur. This could include public spaces, workplaces, classrooms, homes, or other community settings.
• Variety and Diversity : Consider the importance of sampling diverse observation settings to capture a range of experiences, perspectives, and contexts. Avoid over-reliance on a single setting or context, as this may limit the generalizability and richness of your observations.

4. Establish Ethical Guidelines

Given the potential impact on participants' privacy, autonomy, and well-being, ethical considerations are paramount in qualitative observation research. Establishing clear ethical guidelines helps ensure the ethical conduct of your study and protect the rights of participants.

• Informed Consent : Obtain informed consent from participants before initiating any observation activities. Clearly explain the purpose, procedures, risks, and benefits of the study, and allow participants to make an informed decision about their participation.
• Confidentiality and Anonymity : Protect participants' privacy and anonymity by keeping their identities and personal information confidential. Avoid using identifying information in your observations or reporting unless participants explicitly consent.
• Respect for Autonomy : Respect the autonomy and agency of participants throughout the research process. Allow participants to withdraw from the study at any time without consequence and ensure that their decisions are respected without coercion or undue influence.
• Minimization of Harm : Take proactive measures to minimize any potential harm or discomfort to participants arising from the observation process. Be attentive to signs of distress or discomfort and take appropriate steps to address them, including providing support or discontinuing the observation if necessary.

By carefully planning and preparing for qualitative observation, you can lay the foundation for a rigorous, ethical, and insightful study that contributes valuable insights to your field of inquiry.

How to Conduct Qualitative Observation?

Once you've completed the planning phase, it's time to immerse yourself in the field and start collecting data through qualitative observation. Let's take a look at the essential aspects of conducting qualitative observation.

Immersion in the Environment

Immersing yourself in the observation environment is crucial for gaining a deep understanding of the context, culture, and dynamics at play. To effectively immerse yourself:

• Engage with the Environment : Actively participate in the activities and interactions occurring within the observation setting. Immerse yourself in the daily routines, rituals, and social dynamics to gain insider perspectives and insights.
• Observe Unobtrusively : While actively engaging with the environment, strive to maintain a balance between active participation and unobtrusive observation. Avoid drawing undue attention to yourself or disrupting the natural flow of interactions.
• Build Trust and Familiarity : Take the time to build trust and familiarity with the participants and stakeholders in the observation setting. Be approachable, respectful, and non-judgmental in your interactions, allowing participants to feel comfortable and open in your presence.

Building Rapport with Participants

Establishing rapport with participants is essential for gaining their cooperation and obtaining rich, meaningful data. To build rapport effectively:

• Demonstrate Genuine Interest : Show genuine curiosity and interest in the participants' lives, experiences, and perspectives. Listen actively, ask open-ended questions , and express empathy and understanding.
• Respect Cultural Sensitivities : Be mindful of cultural norms, values, and sensitivities that may influence your interactions with participants. Respect their cultural practices, traditions, and beliefs, and avoid imposing your own cultural biases or assumptions.
• Be Transparent and Ethical : Be transparent about the purpose and objectives of your study, as well as the role of participants in the observation process. Ensure that participants understand their rights, including the option to withdraw from the study at any time.

Recording Observations

During observation sessions, recording detailed and accurate observations is essential for capturing the richness and complexity of human behavior and interactions. To record observations effectively:

• Use Multiple Data Collection Methods : Employ a combination of data collection methods , such as field notes, audio recordings, video recordings, photographs, or sketches, to capture different aspects of the observation setting.
• Document Contextual Details : Record not only what is happening but also the context, nuances, and subtleties of interactions. Note the physical environment, social dynamics, non-verbal cues, and emotional expressions that contribute to the overall context of the observation.
• Maintain Objectivity and Neutrality : Strive to maintain objectivity and neutrality in your observations, avoiding personal biases or interpretations. Record observations objectively without filtering or distorting the data to fit preconceived notions or expectations.

Managing Observer Bias

Observer bias refers to the tendency of researchers to interpret observations in a way that aligns with their preconceived beliefs or expectations. To manage observer bias effectively:

• Reflect on Personal Biases : Reflect on your own biases, assumptions, and perspectives that may influence your observations and interpretations. Be aware of how your background, experiences, and beliefs shape your perceptions of the observation setting and participants.
• Seek Diverse Perspectives : Involve multiple observers or researchers in the observation process to mitigate individual biases and enhance the reliability and validity of the observations. Compare and discuss observations to identify and address any discrepancies or biases.
• Triangulate Data Sources : Triangulate your observations with other data sources, such as interviews, surveys , or existing literature, to corroborate findings and minimize the impact of observer bias. Use multiple perspectives and sources of evidence to validate your interpretations.

By immersing yourself in the observation environment, building rapport with participants, recording detailed observations, and managing observer bias, you can conduct qualitative observation effectively and ethically, generating valuable insights into human behavior and social interactions.

Qualitative Observation Examples

Examples of qualitative observation abound in various contexts, offering valuable insights into human behavior, social interactions, and cultural dynamics. Here are a few illustrative examples to showcase the diverse applications and approaches of qualitative observation:

Ethnographic Studies

Ethnographic studies involve immersive, long-term observation of a specific group or community within its natural environment. For example, an ethnographer might live in a tribal community for an extended period, observing their daily routines, rituals, and social interactions. Through participant observation, interviews , and field notes, ethnographers gain deep insights into the cultural beliefs, practices, and norms of the community.

Classroom Observations

In education, qualitative observation plays a crucial role in understanding classroom dynamics, teaching practices, and student behaviors. Researchers may observe classroom activities, interactions between teachers and students, and instructional strategies to identify effective teaching methods and areas for improvement. By capturing the complexity of the learning environment, qualitative observation informs educational policy, curriculum development, and teacher training initiatives.

Workplace Observations

Qualitative observation is also valuable in studying organizational behavior and dynamics within the workplace. Researchers may observe employee interactions, communication patterns, and leadership styles to understand organizational culture, team dynamics, and factors influencing employee satisfaction and productivity. Workplace observations inform management practices, employee training programs, and organizational development strategies to foster a positive work environment and enhance performance.

Clinical Observations

In healthcare settings , qualitative observation studies patient-provider interactions, healthcare delivery processes, and patient experiences. Clinicians and researchers may observe medical consultations, treatment procedures, and patient interactions to identify barriers to effective care, communication challenges, and opportunities for patient-centered interventions. Clinical observations contribute to improving healthcare quality, patient satisfaction , and health outcomes.

Urban Planning and Design

In urban planning and design, qualitative observation helps researchers understand the built environment's impact on human behavior and well-being. Urban planners may observe pedestrian movement patterns, public space utilization, and community interactions to inform the design of cities, neighborhoods, and public infrastructure. Qualitative observation contributes to creating inclusive, accessible, and sustainable urban environments that enhance the quality of life for residents.

These examples demonstrate the versatility and significance of qualitative observation in generating insights, informing decision-making, and addressing complex social phenomena across diverse fields and contexts. Whether studying cultural practices in remote villages, classroom dynamics in schools, or patient experiences in healthcare settings, qualitative observation offers a powerful lens through which to explore the intricacies of human behavior and society.

How to Analyze Qualitative Data from Observation?

Analyzing qualitative data from observation involves systematically organizing, interpreting, and making sense of the rich and nuanced information gathered during the observation process.

Data Coding and Categorization

Data coding and categorization are fundamental processes in qualitative data analysis , enabling researchers to organize and structure the raw data into meaningful units for analysis. To effectively code and categorize qualitative data:

• Open Coding : Begin by engaging in open coding, where you systematically review and categorize the data into initial codes or categories based on recurring patterns, themes, or concepts. This process involves breaking down the data into smaller units and identifying key concepts or ideas.
• Axial Coding : Once you have generated initial codes, use axial coding to establish relationships and connections between codes. Look for patterns, associations, and linkages between different codes, grouping them into broader categories or themes.
• Selective Coding : Finally, engage in selective coding to refine and prioritize the most salient and significant codes or themes that capture the essence of the data. Selective coding involves identifying core themes or concepts that emerge as central to the phenomenon under study and integrating them into a coherent narrative.

Identifying Patterns and Themes

Identifying patterns and themes is a central aspect of qualitative data analysis , allowing researchers to uncover underlying meanings, insights, and relationships within the data. To identify patterns and themes effectively:

• Thematic Analysis : Conduct thematic analysis to systematically identify and explore recurring patterns, themes, or concepts within the data. This involves reviewing the coded data, looking for commonalities, variations, and outliers, and organizing them into meaningful clusters or themes.
• Constant Comparison : Engage in continuous comparison , where you continually compare and contrast different segments of the data to identify similarities and differences. This iterative process allows themes to emerge organically from the data rather than imposing preconceived categories or frameworks.
• Contextual Interpretation : Interpret the identified patterns and themes within the broader context of the observation setting, participants' experiences, and relevant theoretical frameworks. Consider the socio-cultural, historical, and environmental factors that may influence the emergence and significance of the themes.

Integrating Qualitative and Quantitative Data (if applicable)

In some research studies, it may be appropriate to integrate qualitative observation data with quantitative data from other sources to gain a comprehensive understanding of the phenomenon under study. To integrate qualitative and quantitative data effectively:

• Mixed Methods Approach : Adopt a mixed methods approach, where qualitative observation data are triangulated with quantitative data collected through surveys, experiments , or secondary sources. This integration allows for a more holistic and nuanced analysis of the research problem, providing multiple perspectives and insights.
• Complementary Analysis : Analyze qualitative and quantitative data separately to identify unique insights and patterns within each dataset. Then, integrate the findings through comparison, contrast, or synthesis to identify convergent or divergent themes and trends.
• Data Transformation : Transform qualitative observation data into quantitative metrics or variables for comparative analysis with quantitative data. This may involve quantifying qualitative codes or themes into numerical scores or categories for statistical analysis.

Ensuring Data Trustworthiness and Reliability

Ensuring the trustworthiness and reliability of qualitative data is essential for establishing the validity and credibility of the research findings. To ensure data trustworthiness and reliability:

• Credibility : Enhance credibility by employing rigorous data collection and analysis techniques, maintaining detailed documentation of the research process, and engaging in member checking, where participants review and validate the findings to ensure accuracy and authenticity.
• Transferability : Enhance transferability by providing rich, detailed descriptions of the observation setting, participants, and data collection procedures, allowing readers to assess the applicability of the findings to other contexts or populations.
• Dependability : Enhance dependability by ensuring transparency and consistency in the research process, including clear documentation of data collection methods, coding procedures, and analytical decisions. Engage in peer debriefing and external audits to verify the reliability of the findings.
• Confirmability : Enhance confirmability by maintaining reflexivity throughout the research process, acknowledging and addressing personal biases or assumptions that may influence the interpretation of the data. Use transparent and systematic approaches to data analysis, allowing for independent verification by other researchers.

By systematically analyzing qualitative data from observation, researchers can uncover meaningful patterns, themes, and insights that provide rich insights into human behavior, social interactions, and cultural dynamics. By ensuring the trustworthiness and reliability of the data, researchers can generate robust and credible findings that contribute to the body of knowledge in their respective fields.

How to Report Qualitative Observation Findings?

Reporting findings from qualitative observation is a critical step in the research process. It enables researchers to communicate their insights, interpretations, and conclusions to the broader academic community and stakeholders. We'll explore the key considerations for effectively reporting findings from qualitative observation studies.

1. Choose the Right Format

Choosing the proper format for reporting your qualitative observation findings depends on various factors, including the nature of the research, the preferences of the target audience, and the intended impact of the study. Popular formats include:

• Research Papers : Academic journals are a common platform for reporting qualitative observation findings. Research papers typically follow a standardized structure, including an abstract, introduction, methods, results, discussion, and conclusion sections. Choose a journal that specializes in qualitative research and aligns with the scope and focus of your study.
• Reports : Reports provide a more comprehensive and detailed overview of the research findings, including background information, methods, results, and implications. Reports may be distributed to stakeholders, funding agencies, or organizational partners interested in the study outcomes.
• Presentations : Presentations offer an opportunity to disseminate key findings and insights to a broader audience in a concise and engaging format. Consider presenting your findings at academic conferences, professional meetings, or community forums to share your research with peers and stakeholders.

2. Structure the Narrative

Structuring the narrative of your qualitative observation report is essential for guiding readers through the research process and facilitating understanding and interpretation of the findings. When structuring the narrative, make sure to include these elements:

• Introduction : Provide an overview of the research problem, objectives, and significance of the study. Briefly summarize the research design , methods, and approach to qualitative observation.
• Methods : Describe the methods used to conduct qualitative observation, including the observation setting, participants , data collection procedures, and ethical considerations. Provide sufficient detail to allow readers to assess the rigor and credibility of the study.
• Results : Present the key findings and insights derived from qualitative observation. Organize the results thematically, highlighting recurring patterns, themes, or categories that emerged from the data. Use illustrative examples and quotes to support your interpretations.
• Discussion : Interpret and discuss the implications of the findings in relation to the research objectives, theoretical frameworks, and existing literature. Explore the significance of the findings, their practical implications, and areas for further research.
• Conclusion : Summarize the study's main findings and conclusions, emphasizing their relevance and contributions to the field. Reflect on the research's strengths and limitations and offer recommendations for future research or practice.

3. Incorporate Quotes and Examples

Incorporating quotes and examples from the qualitative observation data adds depth, richness, and authenticity to your report, helping to illustrate key themes, insights, and interpretations.

• Selecting Representative Quotes : Choose quotes that capture the essence of participants' experiences, perspectives, and emotions. Select quotes that are vivid, compelling, and representative of the broader themes or patterns identified in the data.
• Providing Contextual Information : Provide contextual information to accompany the quotes, including details about the participant, the observation setting, and the specific context in which the quote was obtained. This helps readers understand the significance and relevance of the quote within the broader narrative of the study.
• Interpreting Quotes : Interpret and analyze the quotes within the discussion section of your report, providing insights into their meanings, implications, and contributions to the overall findings. Avoid simply presenting quotes without analysis or interpretation, as this may limit the depth of understanding for readers.

4. Address Limitations and Future Directions

Every research study, including qualitative observation, has its limitations and areas for improvement. Acknowledging and addressing these limitations is essential for maintaining transparency and credibility in your reporting.

• Limitations : Identify and discuss any limitations or challenges encountered during the research process, such as sample size constraints, data collection biases, or contextual constraints. Be honest and transparent about the limitations of the study and their potential impact on the validity and generalizability of the findings.
• Future Directions : Based on your study's findings, suggest potential avenues for future research or areas for further exploration. Consider unanswered questions, emerging themes, or areas of controversy that warrant further investigation. Offer recommendations for methodological improvements or alternative approaches to address the limitations identified.

By carefully choosing a suitable format, structuring the narrative effectively, incorporating quotes and examples, and addressing limitations and future directions, you can create a compelling and informative report that effectively communicates the findings of your qualitative observation study to the broader academic community and stakeholders.

Conclusion for Qualitative Observation

Qualitative observation is a powerful tool for understanding the richness and complexity of human behavior and social interactions. By immersing researchers in natural settings and allowing them to observe people in their everyday environments, qualitative observation offers unique insights that complement quantitative methods. From uncovering cultural norms and social dynamics to informing policy decisions and program development, qualitative observation has diverse applications across various fields, including academia, healthcare, business, and social services. Embracing the principles of subjectivity, contextuality, and inductive reasoning, qualitative observation empowers researchers to explore the depth and nuance of human experiences, ultimately contributing to our collective understanding of the world around us. Furthermore, as we continue to navigate an increasingly interconnected and diverse world, the importance of qualitative observation only grows. Its emphasis on context, perspective, and interpretation enables researchers to bridge disciplinary boundaries, engage with diverse communities, and address complex social issues. By fostering empathy, cultural competence, and critical thinking skills, qualitative observation not only advances knowledge and scholarship but also promotes social justice, equity, and inclusion.

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Qualitative Research: Observation

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Participant Observation

Photo: https://slideplayer.com/slide/4599875/

Field Guide

• Participant Observation Field Guide

What is an observation?

A way to gather data by watching people, events, or noting physical characteristics in their natural setting. Observations can be overt (subjects know they are being observed) or covert (do not know they are being watched).

• Researcher becomes a participant in the culture or context being observed.
• Requires researcher to be accepted as part of culture being observed in order for success

Direct Observation

• Researcher strives to be as unobtrusive as possible so as not to bias the observations; more detached.
• Technology can be useful (i.e video, audiorecording).

Indirect Observation

• Results of an interaction, process or behavior are observed (for example, measuring the amount of plate waste left by students in a school cafeteria to determine whether a new food is acceptable to them).

Suggested Readings and Film

• Born into Brothels . (2004) Oscar winning documentary, an example of participatory observation, portrays the life of children born to prostitutes in Calcutta. New York-based photographer Zana Briski gave cameras to the children of prostitutes and taught them photography
• Davies, J. P., & Spencer, D. (2010).  Emotions in the field: The psychology and anthropology of fieldwork experience . Stanford, CA: Stanford University Press.
• DeWalt, K. M., & DeWalt, B. R. (2011).  Participant observation : A guide for fieldworkers .   Lanham, Md: Rowman & Littlefield.
• Reinharz, S. (2011).  Observing the observer: Understanding our selves in field research . NY: Oxford University Press.
• Schensul, J. J., & LeCompte, M. D. (2013).  Essential ethnographic methods: A mixed methods approach . Lanham, MD: AltaMira Press.
• Skinner, J. (2012).  The interview: An ethnographic approach . NY: Berg.
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• Last Updated: Mar 1, 2024 10:13 AM
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Understanding qualitative observation

Last updated

20 March 2023

Reviewed by

Tanya Williams

You can easily analyze quantitative data, making it an ideal resource for researchers looking to understand complex topics. But not everything is quantifiable. 

Analyze qualitative observations

Get better insights into customer behavior when you analyze qualitative observation data in Dovetail

• What is qualitative observation?

Because research often relies on data that we can't easily put into objective numbers, researchers rely on non-numerical data for some of their studies.

Qualitative observation is one way of gathering that data. When researchers deploy this method, they collect their data by directly observing the people, behaviors, or events they're studying. 

Qualitative observation relies on a more subjective approach than quantitative analysis, but it remains a powerful tool for suitable areas.

Qualitative vs. quantitative observation

Many people wonder which method is better. Is qualitative observation better than quantitative, or vice-versa? The answer depends entirely on the subject you’re studying.

You should always opt for obtaining hard, objective data when possible. When it isn't possible, qualitative observation can fill in many missing gaps. 

Ultimately, researchers shouldn't view these as two competing methodologies. They are great tools for developing a complete picture of complex topics.

• Characteristics of qualitative observation

Researchers choose qualitative observation to generate meaningful and context-specific insights into social phenomena that are otherwise hard to measure. 

They must rely on distinct characteristics to guide their research and help them interpret the data they uncover to gain accurate insights without quantifiable data.

Here are some of these characteristics:

Naturalistic inquiry

Qualitative observation rarely occurs in controlled laboratory environments, as naturalistic inquiry studies people in their natural context. When people are in a laboratory setting, they are prone to change their behavior. More natural settings put them at ease and make them feel less like study subjects.

Participant observation

To gain a deeper understanding of the topic you’re studying, you can actively participate in the activities or events you’re observing. You learn more about the subject by engaging directly with it and recording your experiences.

Sensitivity to context

Social norms, power dynamics, and historical factors can shape beliefs and behaviors. 

For example, a researcher in a position of power over a participant may make the person feel uncomfortable. Residents of countries with a long history of slavery may find race-related topics more challenging to discuss than in other countries. 

These factors become variables that a qualitative observer must control for. It’s vital to be aware of how a person's experiences might shape their perceptions. 

Reflexivity

Researchers are humans too. They're subject to the same factors that impact their subject's beliefs. Researchers must actively examine how their perceptions and biases may cloud how they interpret the data.

Empathic neutrality

In addition to using reflexivity to examine their biases, researchers must remain neutral and unbiased when conducting their study. They must empathize with the perspectives and experiences of the participants, even when they disagree with them. 

Researchers should aim to understand and appreciate the participants' experiences without imposing their views or judgments on them.

Subjectivity

Objectivity is impossible with qualitative observation. Researchers who employ qualitative observation must be aware of this fact. Acknowledging that they won't gain objective truth about human behavior and experience while using the method is vital. It allows them to be open to alternative interpretations of the data, all of which may be valid.

While engaging in qualitative observations, researchers must recognize the unique aspects of each individual and situation they’re studying. This involves being attentive to the nuances of the data and avoiding generalizations or stereotypes that may overlook the complexity of human behavior and experience.

Inductive reasoning

Studying objective subjects is often deductive: Researchers start with a hypothesis and gather data to test it. Subjective areas of study are the opposite. Inductive reasoning involves gathering and examining the data to develop theories and insights into what they learned.

• What are the types of qualitative observation?

Qualitative observation is a powerful research method you can apply to many situations. As each situation is unique, choosing the right approach is essential. 

You can employ several types of qualitative observation, which all have strengths and limitations:

Direct observation

This method allows researchers to observe and record behavior as it occurs in its natural setting. This type of observation can come in many forms; researchers may casually observe the subject or engage in a more structured, systematic observation. 

Direct observation can be beneficial for studying complex social interactions and behaviors that are difficult to capture through other methods.

Case studies

These involve an in-depth analysis of an individual, group, or event. Researchers collect as much information as possible about the case under investigation, reading interviews, documentation, and more to develop their understanding. 

Case studies are particularly useful for studying rare or unusual phenomena you may not easily observe through other methods.

Researcher as participant

In this method, the researcher becomes part of the group and participates in its activities and interactions. This approach can give researchers a unique way to better understand how the people they're studying feel about a topic. 

However, the researcher's role in the group can influence group behavior. Researchers who use this method must be extra careful to be neutral participants. 

Sometimes, the best way to understand how someone feels about something is simply to talk to them.  Interviews can take two forms:

Structured , with a pre-defined set of questions

Unstructured , with open-ended questions and flexible conversation. 

Well-conducted interviews can provide rich, detailed data about individual experiences, attitudes, and beliefs.

Focus groups

In this type of interview, researchers bring together a small group to discuss a specific topic or issue. They can facilitate the discussion and record what the study participants say. 

Focus groups can provide insights into group dynamics and the range of perspectives and opinions.

• Examples of qualitative observation

Let’s look at real-world examples of how researchers use qualitative observation in different professions.

An ethnographer studying the social dynamics, cultural practices, and relationships within the community might live among the community members and observe their behavior. Doing so can give them a deeper understanding of how the community thinks and operates.

A psychologist studying the subjective experiences of mental illness may want to understand the thoughts, feelings, and behaviors associated with specific disorders. The psychologist can gain insights into how people experience their symptoms through detailed interviews.

A sociologist studying protesters' motivations, perspectives, and social movements might observe and record their behavior. This information will help the researcher understand how individuals and groups approach protests and express their political views.

A teacher may be studying students' learning and communication patterns in the classroom. The teacher can learn how their students work together and alone to solve problems and share knowledge by observing and recording their interactions.

A case study researcher may want to study the experiences, behaviors, and perspectives of a single individual or group, such as a patient with a rare disease. They might conduct in-depth interviews and observe the patient’s behavior. Studying enough examples in this way can help the researcher understand their subject’s unique situation.

A journalist may be researching the stories and experiences of people impacted by a particular issue, such as homelessness or immigration. The journalist can gain insights into their struggles, challenges, and aspirations by conducting interviews.

An artist seeking to capture the unique qualities and character of a particular location or community through their artwork might observe the place or people firsthand. This way, the artist can create artwork that reflects the perspective of the subject matter.

A software development team developing a new product might invite potential users to try it out. Observing their experiences as they navigate the product's various functions means the team can gain important insights. It can help them determine where usability issues may occur and refine the product’s interface or instructions.

Qualitative observation is a great way to generate meaningful insights into subjects where numbers aren’t enough. Using the correct methods means you’ll have rich, valuable data for your study even when it’s not easily measurable.

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23 Qualitative Observation Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Qualitative observation is a method of collecting data that involves describing the attributes and properties of a phenomenon or subject through the senses, rather than using numerical measurements.

This type of observation is most beneficial for studies designed to provide in-depth, detailed, nuanced, and contextualized case studies. The qualitative data generated can help to explain complex concepts.

However, unlike quantitative observation (such as measuring lengths, instances, and intervals), this data cannot be generalized beyond the scope of the research project itself, limiting its applicability to a wide variety of situations.

Qualitative Observation Examples

1. participant observation.

Participant observation is a type of qualitative observation where the researcher not only observes the researched group or individuals, but also actively engages in the activities of the group or individuals [ 1 ] .

This immersion in the activity and culture of the group allows for a deeper understanding, usually over a longer period. It allows the researcher to fully participate in the day-to-day lives and rituals of the participants.

The goal is to gain deeper insights into their social and cultural practices, which can then be explained through thick description.

Participant Observation Example : An anthropologist might live within a remote tribe for a period to understand their lifestyle, beliefs, and customs. They would participate in daily activities, mimic behaviors, and even learn the local language to gather empirical and comprehensive data.

2. Non-Participant Observation

In non-participant observation, the researcher observes the participants without getting involved in their actions or activities [ 2 ] . In other words, they remain “invisible” to the research subjects, who are unaware of being observed.

This reduces potential observer-effects, such as participants modifying their behavior because they know they are being watched. However, it also places some distance between the observer and the observed, which may limit the depth of understanding.

Example: A researcher studying interactions in a workplace might install surveillance cameras and watch the footage to observe how employees interact when they think they are not being observed. By not involving themselves directly, they aim to capture the most accurate and natural interactions.

3. Naturalistic Observation

Naturalistic observation is a method where the researcher observes behavior in a natural setting, without manipulating or controlling the situation [ 3 ] .

It allows the researcher to study things in their natural environment, providing a real-world context. This observational method provides the opportunity to collect data that is not artificial or manipulated while providing deep insights into a phenomenon.

Naturalistic Observation Example : A psychologist studying children’s play behavior might observe a group of children in a playground during their recess. By leaving the children in their natural and uncontrolled play environment, the psychologist can see the real-life application of the children’s social skills, the play schemes they adopt, and how they use their imagination and negotiation skills.

4. Focus Groups

Focus groups involve a group of individuals who participate in a guided discussion about a particular topic. The researchers take notes and discern themes based on these focus group discussions [ 4 , 5 ] .

The group setting encourages participants to share diverse perspectives, generating rich, detailed data. However, the dynamic can be influenced by more dominant individuals, potentially biasing the group’s collective voice.

Focus Group Example : A market researcher seeking feedback on a new product might conduct a focus group with potential consumers. In the discussion, they would ask about the product’s appeal, perceived usefulness, and areas for improvement, gaining insights into consumer preferences and acceptance.

5. Thick Description

Thick description an approach to qualitative observation used specifically in ethnographic research . It involves taking detailed accounts of people’s behavior in their social and cultural context.

This concept was created by Clifford Geertz [ 6 ] , an anthropologist seeking a better method for the analysis of natural phenomena.

Thick descriptions not only detail the behavior but also its context, as well as the intentions and emotions of the actors. Though rich in detail, interpretation of thick description depends on the cultural competence and analytical skills of the researcher.

Thick Description Example : An anthropologist studying an indigenous tribe might use thick description to provide in-depth accounts of their rituals. They would describe not only the actions of the rituals but also the emotions experienced, the social and cultural context, the symbolism involved, the participants’ understanding and interpretation of the ritual, essentially providing a nuanced, multilayered understanding of the tribe’s practices.

6. Structured Observation

Structured observation is a systematic method in observational research where the researcher explicitly decides where, when, and how the observation will take place.

The researcher typically uses previously defined criteria and may have a specific list of behaviors to look for [ 7 , 8 ] . It often involves the use of standardized scales or coding systems to categorize observed behaviors, and it aims at statistical reliability.

Example: A behavioral psychologist studying aggressive behaviors in children after playing violent video games might conduct structured observations by having the children play the games in a set environment (controlled setting, same gaming platform, same game) and then observing for specific aggressive behaviors (like hitting, shouting, throwing objects) based on a previously prepared checklist.

7. Unstructured Observation

Unstructured observation, unlike structured observation, does not involve pre-determined criteria or an exact plan for the observation.

Instead, the observer merely watches the participants in a situation and records whatever they think is relevant or significant [ 8 ] . This approach allows for the discovery of unexpected phenomena but can be more time consuming and may elicit subjective interpretations.

Example: A sociologist studying homeless people’s daily routines might follow several homeless individuals around throughout their day without a specific plan or list of behaviors to observe. They take note of any behavior they believe is significant to understanding the lifestyle and hardships of a homeless individual.

8. Direct Observation

Direct observation involves a researcher being physically present to observe and record behavior as it occurs. The researcher may or may not be visible to the participants [ 9 ] .

Direct observation allows the researcher to collect firsthand data and reduces the chances of missing any relevant information. However, it can also introduce bias as the researcher’s interpretation and presence can affect the situation.

Example: An educator studying classroom dynamics might sit at the back of the classroom to directly observe student behavior, interactions, and engagement. They could observe how different teaching styles affect student attentiveness, participation, and overall classroom behavior.

9. Indirect Observation

Indirect observation is the collection of data through some means other than direct observation; the researcher does not have to be present at the site of observation [ 9 ] .

The researcher can use techniques such as video recordings, photographs, or existing records. This approach lessens potential bias from the researcher’s presence but might miss contexts or non-verbal cues.

Example: A city planner studying traffic patterns might use footage from surveillance cameras placed at various intersections around the city, rather than observing every intersection in person. This method allows them to analyze traffic flow, most frequent peak hours, and violations at different times and days.

10. Continuous Monitoring

Continuous monitoring involves observing a subject or a phenomenon over an uninterrupted period, recording all actions and events as they occur [ 10 ] .

This type of observation provides comprehensive data about a subject’s behavior across different times and situations. However, it can be time-consuming and requires extensive resources.

Example: A wildlife biologist studying the behavior of a certain animal species might set up a live camera feed on the animals’ habitat to continuously monitor their activities. They would track their feeding habits, social behaviors, mating rituals, sleep patterns, etc., to gain a holistic understanding of the species’ lifestyle.

See Also: Types of Qualitative Research

11. Interval Recording

Interval recording is a method where the observation period is divided into smaller intervals, and the researcher records whether or not a specific behavior occurs within each interval [ 10 ] .

This method provides a snapshot of behavior during certain times and is not as time-consuming as continuous monitoring. Still, it might miss occurrences that happen between intervals.

Example: A clinical psychologist studying an individual with Tourette Syndrome might record every 15 seconds whether or not a tic occurs. They might do this over a series of therapy sessions to understand the frequency of the tics and the effect of the therapy.

12. Time Sampling

Time sampling is an observational method where the researcher records behaviors at pre-determined intervals of time, regardless of the event type [ 10 ] .

The researcher chooses specific time periods and determines whether or not the behavior of interest is happening during those times. Time sampling can simplify long observations and reduce the workload, but it might miss events that happen outside the selected time periods.

Example: An educator studying student engagement during a lecture might conduct a time sampling by noting what the students are doing (listening attentively, talking to fellow students, using their phones) at 10-minute intervals throughout the lecture.

13. Narrative Observation

Narrative observation involves recording detailed, descriptive notes about what is being observed. It attempts to capture the full context and detail of events, rather than just specific behaviors [ 9 ] .

This method is unstructured and allows for capturing unforeseen information, as it doesn’t focus only on specific behaviors or events. However, it can also be time-consuming and data may be difficult to analyze systematically.

Example: A social worker trying to understand the dynamics of a dysfunctional family might record detailed narrative observations of family interactions. This could involve noting down not just what is said and done, but also the context, the tones of voice, facial expressions, and any other significant details such as time and physical setting.

14. Anecdotal Records

Anecdotal records provides a detailed description of a significant behavior or event that the observer finds interesting or important [ 10 ] .

The observer might not necessarily be looking for this specific behavior or event in advance, but upon noticing it, they write it down in as much detail as possible, hence creating an “anecdote” about it. This form of observation captures information that might not have been sought, but could be very significant.

Anecdote Example : A teacher noticing a child displaying advanced reading skills might write an anecdotal record about it, describing how the child was reading, which book they chose, and ways they interacted with the material. These records can then be used to tailor the child’s education plan to their advanced skill level.

15. Checklist Observation

A checklist observation is a form of structured observation where the observer has a specific list of behaviors, characteristics, or events that they are looking to observe [ 11 ] .

It allows for systematic collection of data and can provide a quick and efficient way to record and compare information across multiple observations or participants. However, it might miss other relevant information that is not on the checklist.

Example: A human resource manager observing employee performance might have a checklist that includes factors like punctuality, completion of tasks, cooperation with team members, and ability to meet deadlines. The manager would use this to evaluate the employees and identify areas for improvement.

16. Rubrics

Rubrics are a type of observation tool that provides a detailed performance scoring guide. They include criteria that are specific, observable, and measurable [ 12 ] .

Each criterion is evaluated against a performance scale, allowing for consistency among different observers. While rubrics establish clear expectations and standards, developing effective rubrics can be time-consuming.

Example: A physical education teacher evaluating a student’s performance in gymnastics might use a rubric. Criteria might include the technique of different moves, the level of difficulty, the gracefulness and fluidity of performance, and the creativity of the routine.

17. Video Observation

Video observation refers to using video recording as a tool for data collection in observational research. It involves recording the behaviors or events of interest so they can be analyzed at a later time [ 12 ] .

It allows for capturing details that the observer might miss during real-time observation and for re-visiting the observation for further analysis. However, it may also hinder the naturalness of behaviors due to camera awareness.

Example: A sociolinguist studying nonverbal communication might use video observation capturing conversations between participants. Recording would allow the researcher to review and analyze subtle body language, facial expressions, or pauses that might have been missed during live observation.

18. Audio Observation

Audio observation is a method that uses audio recordings to capture information during a research study. Behaviors or interactions are recorded for subsequent analysis [ 12 ] .

This method assists in preserving the verbal aspects of an event but lacks visual data. It is often used in group discussions, interviews, or conversation analysis where auditory information is primary.

Example: A linguist studying dialect variations in a certain region might conduct audio observations by recording conversations between natives of that region. They could then analyze variations in pronunciation, vocabulary, and syntax.

19. Focused Observation

Focused observation is a method where observers focus on specific behaviors, interactions, or events rather than trying to capture everything that occurs [ 13 ] .

These observations often address specific research questions, allowing for a deeper understanding of the focused elements. However, other contextually significant behaviors or interactions might be overlooked.

Example: A researcher studying gender dynamics in office settings might conduct a focused observation on how men and women participate in team meetings—who initiates discussions more frequently, who gets interrupted more, who takes on leadership roles.

20. Longitudinal Observation

Longitudinal observation spans over a long period, allowing researchers to study changes and developments [ 14 ] .

This method is valuable for exploring long-term effects or trends, but it requires a significant commitment of time and resources. Additionally, maintaining the same observation conditions might be challenging over time.

Example: A developmental psychologist studying the effect of parental involvement on school performance might conduct longitudinal observation by regularly observing a group of students from kindergarten through high school, correlating their academic progression with different degrees of parental involvement.

See Also: Longitudinal Research Guide

21. Field Observation

Field observation , often used in ethnographic research, involves observing subjects in their natural environment, or “in the field” [ 1 ] .

Real world settings offer a wealth of contextual richness, allowing researchers to study individuals or groups in a more natural, less controlled manner, often resulting in more honest behaviors. However, uncontrolled environments can introduce uncontrolled variables, offering potential challenges to data interpretation.

Example: An anthropologist studying the subculture of graffiti artists might conduct field observations by spending time at locations where these artists typically work, observing their process, community interactions, and reactions of the general public.

22. Controlled Observation

Controlled observation takes place in a setting where variables can be manipulated or controlled by the observer [ 15 ] .

Often conducted in a lab setting, it allows the observer to establish cause-and-effect relationships by manipulating independent variables and observing their effect on dependent variables.

While this method can provide strong evidence, it may lack ecological validity, as outcomes observed in a controlled setting may not necessarily translate to real-world situations.

Example: A psychologist studying the effects of anxiety on test performance might conduct a controlled observation by administering a test to two groups: one group under normal conditions, and the other under conditions designed to induce anxiety, then observing for any differences in performance.

23. Case Study Observation

Case study observation involves an in-depth examination of a specific individual, group, or event in real-life context [ 16 ] .

It provides a holistic view of the subject and allows for understanding of complex issues. However, findings might not be generalizable due to specificity.

Example: A social scientist interested in understanding the impact of poverty on education might conduct case study observations on children from low-income families. They might observe the child at home, at school, or in other social settings over a period of time to ascertain how their socio-economic status affects their educational opportunities and performance.

See Also: Advantages and Disadvantages of Case Studies

Qualitative vs Quantitative Observation

Qualitative observation delves into understanding underlying meanings and human behavior through non-numerical data collection, while quantitative observation seeks to uncover patterns and establish facts through numerical data collection and analysis.

Qualitative observation is focused on understanding underlying meanings and concepts that govern behavior or situations. It seeks to explore the depth, richness, and complexity inherent in phenomena. On the other hand, quantitative observation aims at drawing conclusions based on numerical data. It relies on measurable data to formulate facts and uncover patterns in research.

The research methodologies employed in qualitative and quantitative observations significantly differ:

• Qualitative methods are often more flexible, allowing for a broader understanding of issues at hand, and are particularly useful in exploring new or complex issues. They provide a narrative or descriptive outcome, which can be instrumental in understanding the context of a particular situation.
• Quantitative methods are rigid but provide a clear-cut answer to a research question. They yield numerical data that can be analyzed using statistical methods to validate or refute hypotheses. The outcome of quantitative research often comes in the form of graphs, charts, or tables showcasing the relationships between different variables.

The choice between qualitative and quantitative observation often hinges on the nature of the research question and the stage of research.

Qualitative observation is generally more suited for exploratory or foundational research where not much is known about the problem. It helps in hypothesis generation by providing insights into the problem.

Quantitative observation, on the other hand, is ideal for confirmatory or validation studies where hypotheses are tested under controlled conditions. It helps in hypothesis testing and contributes to the establishment of facts or the discovery of general laws.

Both types of observation are crucial for a comprehensive understanding of research problems, and a mixed-methods approach, which combines both qualitative and quantitative methods, is often deemed most effective in tackling complex research questions.

Pros and Cons of Qualitative Observation

Qualitative observation is pivotal in understanding complex phenomena by delving into the intricacies of human behavior, social interactions, and cultural norms [ 9 , 11 ] .

Through qualitative observation, researchers can capture the natural setting and the context in which individuals operate, which often leads to more authentic and nuanced findings.

However, it has its limitations.

The very features that make qualitative observation powerful can also be seen as its shortcomings.

The findings from qualitative observation are not generalizable due to the small sample sizes typically employed [ 11 ] .

Additionally, this form of observation is also highly dependent on the skills and biases of the researcher, which can influence the data collection and interpretation process [ 7 ] .

See the table below for a summary of the key pros and cons:

Up Next: An Introduction to Qualitative Research

[1] Seim, J. (2021). Participant observation, observant participation, and hybrid ethnography.  Sociological Methods & Research , 0049124120986209. ( Source )

[2] Handley, M., Bunn, F., Lynch, J., & Goodman, C. (2020). Using non-participant observation to uncover mechanisms: Insights from a realist evaluation.  Evaluation ,  26 (3), 380-393. ( Source )

[3] Carey, A. L., Rentscher, K. E., & Mehl, M. R. (2020). Naturalistic observation of social interactions.  The Wiley encyclopedia of health psychology , 373-383. ( Source )

[4] Cyr, J. (2019). Focus Groups for the Social Science Researcher . Cambridge University Press.

[5] Mishra, L. (2016). Focus group discussion in qualitative research.  TechnoLearn: An International Journal of Educational Technology ,  6 (1), 1-5. ( Source )

[6] Geertz, C. (1973). Thick Description: Towards an Interprative theory of culture.  The Interpretation of Cultures , 3-31.

[7] Damaskinidis, G. (2017). Qualitative research and subjective impressions in educational contexts.  American Journal of Educational Research ,  5 (12), 1228-1233.

[8] Farid, S. (2022). Observation. In  Principles of Social Research Methodology  (pp. 365-375). Singapore: Springer Nature Singapore.

[9] Daniel, B. K., & Harland, T. (2017). Higher Education Research Methodology: A Step-by-Step Guide to the Research Process . Taylor & Francis.

[10] Kirner, K., & Mills, J. (2019). Introduction to Ethnographic Research: A Guide for Anthropology. SAGE Publications.

[11] Bryman, A. (2012). Social Research Methods . OUP Oxford.

[12] Kelly, G. J., & Green, J. L. (Eds.). (2018). Theory and Methods for Sociocultural Research in Science and Engineering Education . Taylor & Francis.

[13] Schneider, N. C., Coates, W. C., & Yarris, L. M. (2017). Taking your qualitative research to the next level: a guide for the medical educator.  AEM education and training ,  1 (4), 368-378. ( Source )

[14] Thomson, R., & McLeod, J. (2015). New frontiers in qualitative longitudinal research: An agenda for research.  International Journal of Social Research Methodology ,  18 (3), 243-250. ( Source )

[15] Islam, M. R., Khan, N. A., & Baikady, R. (Eds.). (2022). Principles of Social Research Methodology . Springer Nature Singapore.

[16] Njie, B., & Asimiran, S. (2014). Case study as a choice in qualitative methodology.  Journal of research & method in Education ,  4 (3), 35-40.

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Qualitative research method-interviewing and observation

Shazia jamshed.

Department of Pharmacy Practice, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan Campus, Pahang, Malaysia

Buckley and Chiang define research methodology as “a strategy or architectural design by which the researcher maps out an approach to problem-finding or problem-solving.”[ 1 ] According to Crotty, research methodology is a comprehensive strategy ‘that silhouettes our choice and use of specific methods relating them to the anticipated outcomes,[ 2 ] but the choice of research methodology is based upon the type and features of the research problem.[ 3 ] According to Johnson et al . mixed method research is “a class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, theories and or language into a single study.[ 4 ] In order to have diverse opinions and views, qualitative findings need to be supplemented with quantitative results.[ 5 ] Therefore, these research methodologies are considered to be complementary to each other rather than incompatible to each other.[ 6 ]

Qualitative research methodology is considered to be suitable when the researcher or the investigator either investigates new field of study or intends to ascertain and theorize prominent issues.[ 6 , 7 ] There are many qualitative methods which are developed to have an in depth and extensive understanding of the issues by means of their textual interpretation and the most common types are interviewing and observation.[ 7 ]

Interviewing

This is the most common format of data collection in qualitative research. According to Oakley, qualitative interview is a type of framework in which the practices and standards be not only recorded, but also achieved, challenged and as well as reinforced.[ 8 ] As no research interview lacks structure[ 9 ] most of the qualitative research interviews are either semi-structured, lightly structured or in-depth.[ 9 ] Unstructured interviews are generally suggested in conducting long-term field work and allow respondents to let them express in their own ways and pace, with minimal hold on respondents’ responses.[ 10 ]

Pioneers of ethnography developed the use of unstructured interviews with local key informants that is., by collecting the data through observation and record field notes as well as to involve themselves with study participants. To be precise, unstructured interview resembles a conversation more than an interview and is always thought to be a “controlled conversation,” which is skewed towards the interests of the interviewer.[ 11 ] Non-directive interviews, form of unstructured interviews are aimed to gather in-depth information and usually do not have pre-planned set of questions.[ 11 ] Another type of the unstructured interview is the focused interview in which the interviewer is well aware of the respondent and in times of deviating away from the main issue the interviewer generally refocuses the respondent towards key subject.[ 11 ] Another type of the unstructured interview is an informal, conversational interview, based on unplanned set of questions that are generated instantaneously during the interview.[ 11 ]

In contrast, semi-structured interviews are those in-depth interviews where the respondents have to answer preset open-ended questions and thus are widely employed by different healthcare professionals in their research. Semi-structured, in-depth interviews are utilized extensively as interviewing format possibly with an individual or sometimes even with a group.[ 6 ] These types of interviews are conducted once only, with an individual or with a group and generally cover the duration of 30 min to more than an hour.[ 12 ] Semi-structured interviews are based on semi-structured interview guide, which is a schematic presentation of questions or topics and need to be explored by the interviewer.[ 12 ] To achieve optimum use of interview time, interview guides serve the useful purpose of exploring many respondents more systematically and comprehensively as well as to keep the interview focused on the desired line of action.[ 12 ] The questions in the interview guide comprise of the core question and many associated questions related to the central question, which in turn, improve further through pilot testing of the interview guide.[ 7 ] In order to have the interview data captured more effectively, recording of the interviews is considered an appropriate choice but sometimes a matter of controversy among the researcher and the respondent. Hand written notes during the interview are relatively unreliable, and the researcher might miss some key points. The recording of the interview makes it easier for the researcher to focus on the interview content and the verbal prompts and thus enables the transcriptionist to generate “verbatim transcript” of the interview.

Similarly, in focus groups, invited groups of people are interviewed in a discussion setting in the presence of the session moderator and generally these discussions last for 90 min.[ 7 ] Like every research technique having its own merits and demerits, group discussions have some intrinsic worth of expressing the opinions openly by the participants. On the contrary in these types of discussion settings, limited issues can be focused, and this may lead to the generation of fewer initiatives and suggestions about research topic.

Observation

Observation is a type of qualitative research method which not only included participant's observation, but also covered ethnography and research work in the field. In the observational research design, multiple study sites are involved. Observational data can be integrated as auxiliary or confirmatory research.[ 11 ]

Research can be visualized and perceived as painstaking methodical efforts to examine, investigate as well as restructure the realities, theories and applications. Research methods reflect the approach to tackling the research problem. Depending upon the need, research method could be either an amalgam of both qualitative and quantitative or qualitative or quantitative independently. By adopting qualitative methodology, a prospective researcher is going to fine-tune the pre-conceived notions as well as extrapolate the thought process, analyzing and estimating the issues from an in-depth perspective. This could be carried out by one-to-one interviews or as issue-directed discussions. Observational methods are, sometimes, supplemental means for corroborating research findings.

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

On This Page:

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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• Published: 22 August 2024

To share or not to share, that is the question: a qualitative study of Chinese astronomers’ perceptions, practices, and hesitations about open data sharing

• Jinya Liu   ORCID: orcid.org/0000-0002-9804-8752 1 ,
• Kunhua Zhao 2 , 3 ,
• Liping Gu 2 , 3 &
• Huichuan Xia   ORCID: orcid.org/0000-0002-0838-7452 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1063 ( 2024 ) Cite this article

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• Social policy

Many astronomers in Western countries may have taken open data sharing (ODS) for granted to enhance astronomical discoveries and productivity. However, how strong such an assumption holds among Chinese astronomers has not been investigated or deliberated extensively. This may hinder international ODS with Chinese astronomers and lead to a misunderstanding of Chinese astronomers’ perceptions and practices of ODS. To fill this gap, we conducted a qualitative study comprising 14 semi-structured interviews and 136 open-ended survey responses with Chinese astronomers to understand their choices and concerns regarding ODS. We found that many Chinese astronomers conducted ODS to promote research outputs and respected it as a tradition. Some Chinese astronomers have advocated for data rights protection and data infrastructure’s further improvement in usability and availability to guarantee their ODS practices. Still, some Chinese astronomers agonized about ODS regarding the validity of oral commitment with international research groups and the choices between international traditions and domestic customs in ODS. We discovered two dimensions in Chinese astronomers’ action strategies and choices of ODS and discussed their descriptions and consequences. We also proposed the implications of our research for enhancing international ODS in future work.

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Citizen scientists—practices, observations, and experience

Perceived benefits of open data are improving but scientists still lack resources, skills, and rewards

A focus groups study on data sharing and research data management

Introduction.

Open data sharing (ODS) emphasizes scientific data’s availability to the public beyond its usability and distribution within academic communities (UNESCO, 2021 ). ODS has become increasingly significant since the Big Data era has engendered a paradigm shift towards data-intensive science (Tolle et al., 2011 ), and ODS has promoted data-intensive science to incorporate all stakeholders, such as researchers, policymakers, and system designers to address data processing and utilization issues collectively (Kurata et al., 2017 ; Zuiderwijk et al., 2024 ). Meanwhile, ODS has improved scientific discovery and productivity since different governments and funding agencies have endorsed ODS and published policies to facilitate it (Lamprecht et al., 2020 ). For example, the UK Research and Innovation (UKRI) issued the “Concordat on open research data” in 2016 to ensure that research data gathered and generated by the UK research community must be openly available to the public (UK Research and Innovation, 2016 ). The Chinese government published a “Scientific Data Management Methods” policy in 2018, requiring government-funded research to share its data with the public (General Office of the State Council of China, 2018 ). Besides such government initiatives, the scientific community has also proposed guiding principles for ODS, such as the “FAIR principles” to facilitate data sharing in respect of Findability, Accessibility, Interoperability, and Reuse (Wilkinson et al., 2016 ).

Astronomy is data-intensive and has long been regarded as a prime model of ODS for other scientific fields. For example, the famous Large Synoptic Survey Telescope (LSST) project has committed to real-time ODS after its start-up in 2025 and has released early survey data since June 2021 (Guy et al., 2023 ). Scholars have conducted a few studies to dig out the good practices of ODS in astronomy and found that ODS has a long tradition in astronomy supported by its well-established knowledge infrastructure and data policies (Zuiderwijk and Spiers, 2019 ; Borgman et al., 2021 ). Still, scholars found that some astronomers were hesitant to conduct ODS due to the high reward expectations (e.g., acknowledgment, institutional yearly evaluation, extra citation) and extra efforts (e.g., additional data description) required in ODS practices (Zuiderwijk and Spiers, 2019 ; Kim and Zhang, 2015 ); some astronomers also raised barriers about the usability and availability of data infrastructure to support ODS practices (Pepe et al., 2014 ).

Despite the ODS tradition in astronomy, researchers’ motivations and barriers to ODS may differ based on their cultural contexts. Most empirical studies of ODS have been conducted in Western and developed countries (Genova, 2018 ). Whether these findings hold in non-Western cultures deserves further exploration. Chinese culture and customs differ from Western ones, which may impose distinctive influences on Chinese people’s perspectives and behaviors. For example, Confucianism often renders Chinese individual researchers stick to collectivism or the societal roles assigned to them (Jin and Peng, 2021 ), which is less common in Western culture or academia to our knowledge. Also, scientific research paradigms have originated from and situated in Western culture for a long time. They call for critical examinations and alternative perspectives at the individual and societal or cultural levels, and ODS has been regarded as an essential lens to deliberate it (Serwadda et al., 2018 ; Bezuidenhout and Chakauya, 2018 ; Zuiderwijk et al., 2024 ).

Besides our concerns about cultural and research paradigm differences, Chinese astronomers’ distinctive characteristics have also motivated us to conduct this study. First, based on our prior experience with some Chinese astronomers, we have observed that Chinese astronomers follow enclosed or independent data-sharing norms that are uncommon to researchers in other disciplines. Their research seems to be more international than domestic. Since a slogan from the Chinese government has influenced many research disciplines (including ours) in China, advocating Chinese scholars to “Write your paper on the motherland” (Wang et al., 2024 ), we wondered how such propaganda would impact Chinese astronomers’ attitudes and behaviors. Second, a recent study has revealed that some Chinese astronomers struggled with ODS because they respected it as a tradition on the one hand and desired to gain career advantages (e.g., more data citations) on the other (Liu J, 2021). This finding contrasts another recent study’s conclusion that Chinese early career researchers (ECRs) (in non-astronomy disciplines) would only welcome ODS if the evaluation system rewarded them (Xu, et al., 2020 ). Hence, we wanted to investigate Chinese astronomers’ motivations and barriers regarding ODS further.

Finally, though ODS has been well-acknowledged internationally, it has not been studied or implemented extensively in most research disciplines in China, with astronomy as a rare exception. Hence, we posited that research about ODS in astronomy might shed light on other research disciplines’ popularization of ODS in China. In addition, previous studies on ODS in China have primarily focused on the Chinese government’s open data policies, infrastructure conditions, and management practices (Zhang, et al., 2022 ; Huang et al., 2021 ). To the best of our knowledge, little attention has been paid to Chinese researchers’ perceptions and practices. Thus, we wanted to conduct an exploratory investigation with Chinese astronomers to fill this gap and foster international ODS and research collaboration in Chinese astronomy and other research disciplines more broadly.

With these motivations in mind, we proposed the following research questions.

How do Chinese astronomers perceive and practice open data sharing?

Why do some Chinese astronomers hesitate over the issue of open data sharing?

To address those research questions, we conducted a qualitative study comprising 14 semi-structured interviews and 136 open-ended survey responses with Chinese astronomers to understand their practices and concerns regarding ODS. We found that many Chinese astronomers conducted ODS to promote research outputs and respected it as a tradition. Some Chinese astronomers have advocated for data rights protection and data infrastructure’s further improvement in usability and availability to guarantee their ODS practices. Still, some Chinese astronomers agonized about ODS regarding the validity of oral commitment with international research groups and the choices between international traditions and domestic customs in ODS. We discovered two dimensions in Chinese astronomers’ action strategies and choices of ODS and discussed these findings and implications. This study makes the following contributions. First, it provides a non-Western viewpoint for global ODS in astronomy and recommendations for advancing global and Chinese ODS policies and practices. Second, it reveals Chinese astronomers’ concerns, motivations, and barriers to conducting ODS. This may inspire domestic government, international research policymakers, and ODS platforms and practitioners to empathize with and support Chinese astronomers. Finally, this study may shed light on implementing ODS in other research disciplines in China, which has not been popular.

Literature review

The background of ods in science.

The open data movement in scientific communities was initiated at the beginning of the 21st century (e.g., Max Planck Society, 2003) (Tu and Shen, 2023 ). ODS, also known as open research data, advocates that the openness of scientific data to the public is imperative to science (UNESCO, 2021 ; Fox et al., 2021 ). Prior research has inquired about researchers’ intrinsic and extrinsic motivations for ODS. Intrinsic motivations include personal background and ethical perspectives. For example, a researcher’s personal background (research experience, gender, position, age, etc.) has been found to affect their ODS preferences, and significant differences have been observed in research experience (Zuiderwijk and Spiers, 2019 ; Digital Science et al., 2024 ). Also, a researcher’s ethical stance influences their ODS practices. Some researchers conduct ODS because they want to benefit the research community and promote reciprocity among data stakeholders, such as data producers, funders, and data users (Lee et al., 2014 ; Ju and Kim, 2019 ). Extrinsic motivations for ODS include incentive policies, data infrastructure, and external pressures from funders, journals, or community rules. Incentive policies, such as the promise of data citation and the rewarding credit from their institutions, effectively enhance ODS (Dorch et al., 2015 ; Popkin, 2019 ). Also, a well-established infrastructure could facilitate ODS by reducing its cost (Kim and Zhang, 2015 ). Moreover, regulations from researchers’ stakeholders (e.g., journals and funders) press their ODS practices as well. One example is developing data policies. Kim and Stanton proposed that journal regulative pressure has significantly positive relationships with ODS behaviors (Kim and Stanton, 2016 ).

Despite the motivations, researchers in ODS still have valid justifications for not conducting such practices (Zuiderwijk et al., 2024 ; Boeckhout et al., 2018 ). Sayogo and Pardo categorized those barriers into (1) technological barriers, (2) social, organizational, and economic barriers, and (3) legal and policy barriers (Sayogo and Pardo, 2013 ). More specifically, at the individual level, Houtkoop et al. found that ODS was uncommon in psychology due to psychologists’ insufficient training and extra workload (Houtkoop et al., 2018 ). Meanwhile, Banks et al. indicated that researchers in organizational research were afraid of exposing the quality of their data (Banks et al., 2022 ). In addition, researchers’ ethical concerns also influence their ODS practices, primarily privacy and fairness issues. Walsh et al. identified the privacy risks related to identity, attribute, and membership disclosure as the main ethical concerns about ODS (Walsh et al., 2018 ). Anane et al. worried that ODS could compromise fairness because some new or busy researchers might lose their data rights during the critical post‐first‐publication period (Anane-Sarpong et al., 2020 ). At the societal level, inadequate data policies have failed to guarantee researchers’ data rights, and property rights are unclear. Enwald et al. proposed that researchers in physics and technology, arts and humanities, social sciences, and health sciences were concerned about legal issues (e.g., confidentiality and intellectual property rights), misuse or misinterpretation of data, and loss of authorship (Enwald et al., 2022 ). Anane et al. found that data ownership was a crucial barrier affecting public health researchers’ willingness to share data openly (Anane-Sarpong et al., 2018 ).

The factors that influence astronomical ODS practices

Astronomy has been a prime example of ODS practices in scientific communities (Koribalski, 2019 ). For example, in gamma-ray astronomy, astronomers have explored how to render high-level data formats and software openly accessible and sharable for the astronomical community (Deil et al., 2017 ). In space-based astronomy, ODS has been an established norm in its research community for a long history (Harris and Baumann, 2015 ). In the interdisciplinary field of astrophysics, evidence has shown that papers with links to data, which also represent an approach of ODS, have a citation advantage over papers that did not link the data (Dorch et al., 2015 ). Additionally, many data archives in astronomy have been openly accessible to the public to increase their reusable value and potential for rediscovery (Rebull, 2022 ).

Prior studies have examined the socio-technical factors fostering ODS. Data policies support ODS implementations, and existing data infrastructure plays an essential role in ODS practices in astronomy (Pasquetto et al., 2016 ; Genova, 2018 ). For example, Reichman et al. attributed astronomy’s long tradition of ODS to its extensive and collaborative infrastructure (e.g., software and data centers) (Reichman et al., 2011 ). In practice, some famous astronomy organizations have built solid data infrastructures to support ODS, such as NASA Astrophysics Data System (ADS) and the International Virtual Observatory Alliance (IVOA) (Kurtz et al., 2004 ; Genova, 2018 ). Astronomy’s integrated knowledge infrastructure spanning decades and countries, encompassing observational data, catalogs, bibliographic records, archives, thesauri, and software, prompts global ODS among astronomers (Borgman et al., 2021 ). Many astronomers have a strong sense of duty to their research communities and the public. Thus, they would accept requests for data to assist colleagues and facilitate new scientific discoveries, which enhances ODS (Stahlman, 2022 ). Besides, astronomers perceived reciprocity influences their ODS practices. They aspire to improve their research outputs’ visibility and contribute to new, innovative, or high-quality research via ODS (Zuiderwijk and Spiers, 2019 ).

Still, some factors may hinder astronomers’ ODS practices. At the individual level, ODS may bring them extra learning load and academic reputation risks. For example, if astronomers perceive challenges in ODS or feel they need to acquire further knowledge, they may be less inclined to engage in such practices (Gray et al., 2011 ). Additionally, astronomers expressed concerns about the possibility of others discovering mistakes in the data (Zuiderwijk and Spiers, 2019 ). Pepe et al. also showed that the difficulty of sharing large data sets and the overreliance on non-robust, non-reproducible mechanisms for sharing data (e.g., via email) were the main hindrances to astronomers’ ODS practices (Pepe et al., 2014 ). At the societal level, an exponential increase in astronomical data volume has led to a continuous enrichment of utilization scenarios. ODS may involve data privacy or national security issues, especially when such data is integrated with other datasets. Thus, Harris and Baumann regarded the primary concern in global ODS as safeguarding national security and establishing appropriate licensing mechanisms (Harris and Baumann, 2015 ).

The development of ODS in China

The Chinese government has recognized ODS as a national strategy in both scientific and public service domains. They issued the “Scientific Data Management Methods” in 2018 and “Opinions on Building a More Perfect System and Mechanism for the Market-oriented Allocation of Factors” in 2022. These policies require that data from government-funded research projects must be shared with the public according to the principle of “openness as the norm and non-openness as the exception” (General Office of the State Council of China, 2018 ; General Office of the State Council of China, 2024 ). The Chinese government applied the “hierarchical management, safety, and control” concept as ODS arrangements to realize a dynamic ordered open research data at the social level (Li et al., 2022 ).

At the institutional level, the Chinese Academy of Sciences (CAS) has been actively promoting infrastructure construction and institutional repositories to support ODS. For example, CAS has affiliated eleven out of twenty national-level data centers that are foundational for ODS in China since 2019. Meanwhile, many Chinese journals have published data policies requesting that researchers append their papers with open-access data. The National Natural Science Foundation of China (NSFC) has funded over 6000 data-intensive research programs, encouraging ODS among them in compliance with the NSFC’s mandate (Zhang et al., 2021 ). Regarding Chinese researchers’ attitudes and practices toward ODS, Zhang et al. have observed that Chinese data policies have shifted from focusing on data management to encompassing both data governance and ODS. This shift has shrunk the gap between Chinese researchers’ positive attitudes toward ODS and their less active ODS behaviors (Zhang et al., 2021 ). Driven by journal policies, Chinese researchers’ ODS behaviors have been encouraged. For example, Li et al. found that more than 90% of the published dataset of ScienceDB is also paper-related data and proposed that the pressure from journals has been the main driving force for researchers to conduct ODS (Li et al., 2022 ). ScienceDB (Science Data Bank) is a general-purpose repository in China that publishes scientific research data from various disciplines (Science Data Bank, 2024 ).

Methodology

We conducted a qualitative study comprising 14 interviews and 136 open-ended survey questions with Chinese astronomers from 12 institutions. Our interview questions were semi-structured. Some were framed from the existing literature, and others were generated during the interviews based on the interviewees’ responses. Our open-ended questions are extended from a recent survey on data management services in Chinese astronomy (Liu, 2021 ). Table 1 depicts the formation of our interview questions that served as the major source of our research data. We acknowledge that the interviewees’ responses could be influenced by questions and context during the interview and tried to avoid such biases with the following strategies. First, although Chinese astronomers were hard to contact and recruit, we did our best to diversify our interview sample. Our interviewed Chinese astronomers included researchers and practitioners in observatories, scholars and Ph.D. students in astronomy at top universities in China, and researchers in astronomical research centers. Second, we conducted our interviews in different contexts, such as on campus, in observatories, at research centers, and over phones. Thus, we tried to de-contextualize our interview questions to reduce potential biases. Finally, our qualitative data and analysis were not only from interviews but also from our previous survey. We used the interview and survey data to corroborate and complement each other.

Data collection and analysis

Our interviews were conducted in person or via WeChat video. They lasted 30–45 min and were recorded and fully transcribed. Our recruitment was challenging and time-consuming due to COVID-19 and the limited number of Chinese astronomers available for the interview. We have obtained their informed consent and have followed strict institutional rules to protect their privacy and data confidentiality. In addition, we conducted a survey using the online platform ‘Survey Star’ and obtained responses from 136 Chinese astronomers. For the scope of this paper, we focus on reporting qualitative data.

We kept our first round of data analysis, including notetaking and transcription, simultaneous with the interview progress. Meanwhile, we have fully transcribed and translated the interview recordings in Chinese into verbatim in English. As for the data analysis part, we employed the thematic analysis technique to extract and analyze themes from the interview transcripts (The interviewees are numbered with the letter P) and open-ended survey responses (The survey responses are numbered with the letter Q). Thematic analysis is well-suited for analyzing interview transcripts and open-ended survey responses (Braun and Clarke, 2006 ). We referenced Braun and Clarke’s recommended phases and stages of the analysis process (Braun and Clarke, 2006 ). First, we read through transcriptions and highlight meaning units. Simultaneously, we conducted coding and identified participants’ accounts, which were presented in the form of notes. Second, we categorized the codes and subsequently attributed them with themes that corresponded to ethical concerns. Third, we verified the themes by having them reviewed by two additional authors to ensure high accuracy in our analysis. Finally, we linked our themes with existing literature to provide a more comprehensive narrative of our findings. Table 2 lists the demographic information of the interviewees.

We referenced Stamm et al.’s work to categorize the career stages of the Chinese astronomers we interviewed (Stamm et al., 2017 ). As shown in Table 2 , Most interviewees fall into the Senior-career stage because they have rich research experiences and resources in ODS.

Three types of Chinese astronomers’ behaviors at different ODS stages

We categorize the Chinese astronomers’ ODS behaviors into three types at different stages of ODS. First, Chinese astronomers mentioned that one type of ODS behavior is making the data publicly available on a popular platform (e.g., Github, NASA ADS, arXiv) or data centers after the proprietary data period has expired. The proprietary data period, or the exclusive data period, refers to the time between researchers first accessing the data and publishing their findings. This period typically ranges from one year to two years in astronomy, which aims to cover a normal and complete astronomical research cycle. P13 explained:

The data is not in our hands. After we use the telescope to complete the observations, the data will be stored in the telescope’s database. During the proprietary period (12 months), only you can view it. After the proprietary data period has passed, anyone can view it. (P13)

She meant that the raw data produced by astronomers were stored by the builders, who were also responsible for making those data visible to the public when the proprietary data period had expired. Zuiderwijk and Spiers’s survey has also revealed that astronomers seldom store raw data due to their inability to build a data center. Consequently, astronomers often do not influence data-sharing decisions directly but only propose data collection ideas (Zuiderwijk and Spiers, 2019 ).

Secondly, Chinese astronomers also regraded sharing the data with research teams or individuals upon their requests during the proprietary data period, which is also feasible. For example, P5, said:

I published one paper using research data whose proprietary period hasn’t expired. If someone emailed me to inquire whether they could obtain the data for “Figure 2” [here P5 referred to an exemplary figure in her previous publication]. I usually send the data to them. It is common [in astronomy] to communicate with the author via email to consult their willingness toward ODS. (P5)

P5 assumed that sharing data privately was allowed and common among astronomers when the proprietary data period had not yet expired. To some extent, P5 also transformed this private approach toward a visible approach by making his processed data public and publishing it on open platforms.

P11 added the reason why astronomers used this private approach:

The data is not immediately made available. There is a proprietary data period of one or two years. Priority is given to the direct contributors to use the data and produce the first batch of scientific results. After the proprietary data period has expired, others were allowed to discover the value of the data jointly…Other astronomers may also be interested in the data during the proprietary data period. After all, during this period, others were unable to conduct observations and produce data. (P11)

P11 explained that during the period when he applied for observation, others could not produce the data by using the same telescope. However, they might still be interested in such data. Thus, he might share their research data privately with other astronomers if he deemed it necessary for the other astronomers’ research.

Finally, besides the open sharing of research data, two other astronomers also introduced the third type of ODS behavior, the open sharing of research software, tools, and codes. P12 explained:

When the project was completed, project funders required all the research data to be submitted to a certain location for public use. We also needed to submit the software, tools, and related codes developed by astronomers. (P12)

According to P12, ODS is not merely about data per se but also its associated processing tools and accompaniment.

Another astronomer, P10, mentioned that astronomers may also share their software openly to enhance their research influence. P10 said:

Astronomers may openly share their programs in theoretical research and data simulation, particularly simulation programs or source files. They create open-source materials related to their articles and then make their software or related models available online. They also require acknowledgment if someone uses them later. Nowadays, many astronomers use this method for ODS. (P10)

Individual factors concerning Chinese astronomers’ motivations for ODS

Ods is a tradition and duty.

Twelve Chinese astronomers also mentioned that ODS was a traditional norm in astronomy, and they have been obeying it since they entered this scientific field. P11 said:

We have known a traditional norm since we started working in this field. That is, every time you apply for telescope observations and obtain data, this data must be made public one year later. Even if you have not completed your research or published a paper by then, the data will still be made public. For us astronomers, ODS is a natural practice and meaningful endeavor. We believe that astronomy is a role model of ODS for other research fields to follow. (P11)

Four Chinese astronomers also introduced the influence of the tradition of ODS on their motivations for ODS. For example, P10 said:

In the past, I have obtained data of my interest from other astronomers by emailing them. Therefore, if someone approaches me for data, I would also be willing to provide it. (P10)

Another two astronomers elaborated that they acknowledge the ODS tradition due to its benefit to both astronomers and telescopes. P1 said:

According to the international convention, to promote the influence of the telescope and enrich its research outputs, the data is released to the public based on different proprietary data periods. Each data release includes not only raw data but also data products generated by technical personnel processing the raw data. (P1)

I do not process raw data; instead, I typically utilize data products generated by telescopes. These data products, which are openly available in the public domain, assist individuals like me who lack technical expertise in processing raw data to conduct scientific research. Thus, we must also acknowledge the telescope’s contribution when publishing our findings. This is the norm in astronomy. (P13)

P1’s and P13’s opinions were common, which elaborated that telescopes have offered astronomers different kinds of data, enhancing their potential research outputs. In return, when researchers utilize the data generated by telescopes, they also contribute to the telescope’s influence and reputation.

It is worth noting that this tradition is also in telescopes’ data policies, which influences Chinese telescopes’ data proprietary periods setting. For example, the Chinese astronomy projects LAMOST and FAST release data policies that mention the proprietary data period following international conventions. As indicated by P6, the international convention typically observes the proprietary data period of six months to one and a half years.

Six Chinese astronomers believed that ODS is an established tradition in astronomy and ought to be respected and enacted as a duty without considering external factors or consequences. For example, P8, mentioned that:

Astronomy is a very pure discipline without economic benefit, and we have the tradition of ODS. Therefore, they state their data source or post a link to their data directly. My willingness to conduct ODS is also influenced by this atmosphere. Besides that, I regard ODS as a basic requirement because data should be tested [via ODS]. (P8)

Another two astronomers considered ODS in astronomy the nature of science, which motivated them to pursue the goal of openness persistently. For example, P11 said:

Astronomy exemplifies a characteristic of being borderless, where there is a strong inclination towards open academic exchange and sharing of resources and tools. Additionally, astronomy is pure due to its non-profit nature. Thus, astronomers have always maintained simplicity, leading to a culture of openness. (P11)

ODS brings beneficial consequences

Still, four Chinese astronomers hoped to improve their research influence and citations through ODS, especially the research to which they had devoted the most effort. For example, P10 said:

Astronomers not only release their data but also the software or code to process it. This is because if other astronomers use my software and code to process the data, they would also cite the papers with my shared software and code. This will increase the influence of my papers and software or code. (P10)

A similar perspective came from our survey responses Q19, Q22, Q34, and Q47, who also perceived that ODS could improve the research impact of their papers and data. For example, Q22 stated:

I have encountered situations where other researchers requested access to my data. One of the reasons I am willing to share data [with them] is to increase my paper citations. (Q22)

Additionally, some Chinese astronomers practiced ODS to replicate and validate their research. For example, Q26 said:

The primary reason I endorse ODS is to replicate my data analysis by peers and enable independent verification of my research outputs. (Q26)

ODS engenders reciprocity and collaboration opportunities

Fourteen Chinese astronomers mentioned that ODS could increase their research outputs and provide possibilities to obtain other astronomers’ data, thereby promoting the prosperity of research outputs in the entire astronomy community. More importantly, they have established a new type of collaborative opportunity through ODS when data are sufficient but resources/capacities to utilize data are limited. For example, P12 expressed that ODS had a positive impact on the research outputs of the scientific community:

An astronomer I respect once stated that initially, they wanted to conceal all research data, but this proved impossible due to the vast amount of data produced by the telescope. As a result, they released all the data from their large-scale projects. The outcome of this ODS behavior rendered explosive growth in research outputs. (P12)

Another two astronomers noted that ODS was essential to cultivate more astronomers to form collaborative efforts to increase research outputs in the scientific community. P6 said:

The data generated by telescopes used to observe transient events have not been subject to the proprietary data period. Once I observe such events, I will encourage other researchers to join in and rapidly identify these unexpected phenomena, facilitating subsequent observations using various telescopes to maximize scientific output as quickly as possible. (P6)

P6 elaborated that astronomers rely on collaborative efforts for special observations, such as discovering new stars, which maximizes the utilization of global telescope resources. This motivation strengthens collaborations among astronomers from different research teams. P14 added:

New events [e.g., new star discoveries] in astronomy often occur in transience. If I do not share information about these events, other astronomers will not know about them. With limited resources, I may be unable to observe them through other telescopes. However, sharing preliminary data about these events can maximize global resources. This allows for a collaborative effort to observe the event using resources from around the world. (P14)

P14 stated that ODS has the potential to appeal to more astronomers to research contributions through their subsequent and collective efforts based on the initial observation. P14’s opinion echoed Reichman et al.’s findings, which revealed that extensive and collaborative infrastructure was the primary driver behind the adoption of ODS (Reichman et al., 2011 ).

Prior research also indicated that limited resources and capacities would increase collaboration among astronomers in astrophysics research (Zuiderwijk and Spiers, 2019 ). A similar opinion also arose from our survey responses Q18, Q30, and Q52. For example, Q30 said:

I am good at processing data instead of writing papers. ODS can allow me to collaborate with someone who is good at writing papers to co-produce the research output. (Q30)

Societal factors concerning Chinese astronomers’ barriers to ODS

The limitations of verbal agreements in international collaboration.

Although most Chinese astronomers endorsed ODS, three were concerned about other astronomers who might have violated their initial commitments to using data for scientific purposes. For example, P7 commented:

I used to have experiences with foreign collaborators who violated their initial commitments, resulting in unpleasant consequences. Specifically, they promised in emails that they would process the data using a different approach from ours. However, they ended up using the same method and perspective as ours. There was not much to be said about it, as it was not illegal or against data policies’ regulations. It is a matter of trust and promises, and all I can do is not share data with them in the future. (P7)

P10 also added that often, the astronomers’ commitment to email correspondence had to rely on their self-discipline to materialize:

If the proprietary data period has not expired and you share the data with others, you have no control over what they do with it except to trust their promise in the email. This situation relies on the self-discipline of astronomers. (P10)

Three astronomers were also concerned about the validity of oral agreements about ODS. They referred to them as “gentlemen’s agreements.” For example, P14 explained:

In principle, data can be shared with others without a signed contract between us but based on the so-called gentleman’s agreement. Thus, some Chinese astronomers may not be willing to make their research data public because they must assume that everyone is a gentleman [to keep their promise], which may not always be the case as there are also scientists who are not accountable due to a highly competitive environment [in science]. (P14)

P14 regarded the “gentlemen’s agreements” as effective only to those who acted in good faith in fulfilling their commitments. They would not impose or presuppose any “ethical” constraints on collaborators. Hence, he noted that some astronomers were unwilling to share data openly within the proprietary data period because they did not trust the other astronomers’ accountability to fulfill their “gentlemen’s agreements.” Besides that, P6 explained the reason that astronomers have broken their commitments. He said:

In astronomy, some data policies have not been effectively constrained because it is impossible to encompass all subsequent data usage and collaboration situations at first…Also, there are many astronomy alliances. If you are not part of our alliance, you are not bound to commitments, which may lead to disputable issues. (P6)

Data is too dear to share immediately

Ten Chinese astronomers considered that the data they obtained possessed unique scientific values that could contribute to their publication priority and prolificity. Given the fact that publication priority, authorship order, and quantity are still the most important and prevalent factors in evaluating a scholar in China, it becomes comprehensible that these astronomers have expressed concerns about the risk of losing the ‘right of first publication’ if they openly share their processed data too soon. For example, P9 confessed:

I am unwilling to conduct ODS primarily because my research findings have not been published yet. I am concerned that ODS might lead to someone else publishing related findings before I do. (P9)

Similar concerns were also expressed in our survey responses Q42, Q46, and Q53. Q53 provided a more detailed explanation:

The individuals or organizations that produce data should have the right to use it first and only make it publicly available after a round of exploration and the publication of relevant research results. If the data is shared openly and completely from the outset, the number of people or organizations willing to invest time and money in obtaining data in the future will decrease since they can use data obtained by others instead of acquiring it by themselves. (Q53)

Another astronomer, P12, held a negative attitude toward ODS at the early stage of research because he was concerned that their data processing capacity was slower than the other research groups once the data was shared with them:

I put a lot of effort into processing data, and if my research findings have not been published but I release my data in three months [some international rules recommend astronomers to open their data as soon as possible], then someone with a more sophisticated data processing software may be able to write and analyze their research paper within a week because they already have the complete workflow prepared. This may upset the sharers who intended to publish a similar finding, as their work has been done so quickly [sooner than the sharer]. (P12)

A similar opinion could be seen in our survey response Q46:

The scientific community should ensure that those who have worked hard to produce the data also have the priority to publish their research findings before the data has been made publicly available. (Q46)

The disparities between the Chinese and foreign research infrastructures

Five Chinese astronomers expressed their concerns about the disparities between the Chinese and foreign research infrastructures. For example, P9 expressed his concern that adhering to international rules in astronomy might contradict the domestic rules in China due to national security and data confidentiality considerations. He said:

International organizations hope our country will lead in ODS, which may sometimes harm our interests. This is especially the case for the data produced through Chinese telescopes, which are published in international academic journals upon the international journal publishers’ requests because this data may involve confidential engineering tasks in Chinese telescopes that are subject to national security purposes. (P9)

Another astronomer, P4, also mentioned that astronomical data may include equipment parameters that may trigger national security concerns. Hence, she has undergone desensitization before conducting ODS:

Astronomical raw data are generated by the equipment directly and are categorized as first-level data [machine-generated data] in the data policies. More importantly, raw astronomical data should be processed before being opened to the public because the raw data may raise [national] security concerns and leakage equipment parameters. (P4)

P4’s concerns about national security are also reflected in China’s national data policies. For example, the Chinese government mandates the “hierarchical management, safety, and control” policy to supervise ODS to balance its order and dynamic (Li et al., 2022 ).

P8 added that Chinese astronomers are sometimes limited by national rules and domestic data infrastructure usability and accessibility. P8 said:

In some Chinese astronomical projects, only certain frequency bands are internationally permitted, and the first to occupy them claims ownership. Moreover, our data storage and ODS are limited by technical difficulties. We don’t have ODS platforms like NASA ADS. Even if there are, these platforms are currently not as recognized internationally as those abroad. Therefore, when astronomers publish papers or data, they default to submitting them to international platforms. (P8)

Societal factors concerning Chinese astronomers’ hesitations for ODS

The pressure from domestic data policies.

Five Chinese astronomers have mentioned that ODS is subject to the requirements of domestic data policies. Thus, they sense the pressure to conduct ODS. For example, P6 indicated that many astronomy projects in China were government-funded and required data sharing and submission conforming to government regulations as the priority.

Chinese telescopes are primarily funded by the government, as researchers have not yet had the ability to build a telescope on their own. The entire Chinese population is considered one collective, while those non-Chinese are another. The Chinese government aims to promote ODS to data generated by projects funded by public funds. If researchers have not submitted research data to the government-delegated data center, it could potentially impact their subsequent research project approval. By contrast, some foreign telescopes are built by private institutions and may not have the option for ODS. (P6).

Another astronomer, P3, proposed that Chinese mandatory data policies prompt the ODS scale. However, complicated troubles remained.

Our data policies are mandatory, especially for projects funded by national grants. That is, if you don’t conduct ODS, your projects may not be accepted. The volume of ODS is rising consequently. However, the issues related to ODS still need to improve, such as the Chinese astronomers’ initiative willing to ODS is weak, and [sometimes] their open data cannot be reused. There is a need further to investigate Chinese researchers’ [ODS] behaviors, particularly to find the stimulations for them to conduct ODS proactively. (P3)

Besides, three Chinese astronomers shared that the traditional funding source in astronomy also motivated their ODS. P8 explained:

In China, astronomical data [from national telescopes] is mostly institutional and collective. One can apply to use a telescope at a particular institution to obtain astronomical data. The applications may receive different priorities, but the data is not privately owned. (P8)

P8 meant that Chinese astronomers relied on large telescope projects funded by the government. Consequently, the ownership of their observed data belongs to the collective astronomical community in China rather than individual astronomers or research teams.

The language prerequisite in astronomy

Three astronomers have also introduced the issue of a language prerequisite in scientific communication. For example, P12 explained:

[Modern] astronomy predominantly originated from developed nations. Consequently, our conferences, data, and textbooks are primarily in English. However, this can be a barrier for young Chinese astronomers who are not proficient in English. At least among the researchers around me, everyone contends that English is a necessary prerequisite for entering the field of astronomy. That is to say, the entry barrier for astronomy is very high. I termed it “aristocratic science” because it is difficult to conduct astronomical research without good equipment, proficient English, or substantial funding. (P12)

Another astronomer, P9, dismissed astronomical journals in Chinese because these journals would not be acknowledged in the international astronomy community:

I believe English is a strict prerequisite in astronomy. If your English is poor, you may be restricted from engaging in ODS communication. I support [the slogan] publishing in Chinese to enhance Chinese scholars’ international influence, but most astronomical research originates from the West and is primarily dominated by Western institutions. Besides that, domestic journals are not valuable enough for academic evaluation or promotion due to their low influence factor. (P9)

Finally, P13 added that if Chinese astronomers always use English in ODS, it might potentially clash with the academic discourse system in China.

Some people may wonder why, as Chinese researchers, we need to use English to communicate our work. From my personal perspective, of course, I fully support promoting our research discourse system using Chinese as the primary language. However, from a [scientific] communication standpoint, there are times when we need to collaborate with foreign astronomers or improve communication efficiency [in English]. (P13)

The awareness of a competitive environment

Four Chinese astronomers have expressed concerns about ODS due to the highly competitive scientific community to which they belong. For example, P14 stated:

The field we are currently working in is highly competitive, so we need to consider protecting our team’s efforts. If we release the data, there is a possibility that other researchers using more advanced software tools could publish their findings before us. (P14)

Another astronomer, P12, remarked that this competitive atmosphere varies depending on the research directions. He said:

Competition is inevitable but varies across research areas. I engaged in two research areas. One is characterized by intense competition, but the other is more friendly. The highly competitive research area has many researchers pursuing high-quality data and tackling cutting-edge topics. Sometimes, competing with those who publish first or faster becomes necessary. In addition, one kind of “Nei Juan” may exist, which is competing to see who can open data faster. Because the faster your proposal is promised, the sooner your observation project will be approved. (P12)

“Nei Juan” (a.k.a. involution) manifests a fierce but often unfruitful competition to catch up with colleagues, peers, and generations (Li, 2021 ). P12 acknowledged the competitive environment that would push him to publish first or faster but also regarded “Nei Juan” as not always bad for ODS. Still, P9 considered that the “Nei Juan” issue may arise because Chinese astronomers want to catch up with the international astronomical development phase.

Generally speaking, astronomy is relatively less “Nei Juan” compared to other disciplines. However, its rapid development has begun to become more intense. Particularly, Chinese astronomy is in a phase of catching up, characterized by a collaborative yet competitive atmosphere with the international community. Our national astronomical teams, as a collective, are exerting great efforts to excel in some major projects compared to their foreign counterparts, engaging in strenuous research endeavors. (P9)

However, another astronomer, P11, regarded that ODS meant not “the sooner, the better.” P11 argued:

Some data may have been obtained through instrument testing, and its quality is not particularly high, resulting in lower reliability. If it is made openly accessible immediately, users may not obtain accurate results. Besides, the raw data may contain variances or noises originating from different instruments, requiring standardized processing through software to transform it into [reliable] data products. Only then can scientific users and the public truly benefit from this data. (P11)

The interpretation of Chinese astronomers’ ODS motivations and behaviors

Chinese astronomers’ motivations and behaviors in ODS can be interpreted threefold. First, a few Chinese astronomers’ obedience to ODS is traditional. They value the tradition of ODS in astronomy and contend that it should be respected and obeyed as an intrinsic duty (Heuritsch, 2023 ). Also, they acknowledge the value of astronomical ODS practices for scientific research and the whole scientific community, which makes them devote themselves to such practices (e.g., P8, P12). Hence, for them, extrinsic principles (e.g., FAIR), policies (e.g., those from the Chinese government), or individual research outputs do not determine their ODS decisions and behaviors. As P11 said, he had learned and obeyed this tradition since he entered the field of astronomy. This finding in China corroborates Stahlman’s prior research, indicating that astronomers have a strong sense of duty to their research communities and the public (Stahlman, 2022 ). Still, we found it impressive because these Chinese astronomers adhere to ODS traditions, dismissing the government slogan “Write your paper on the motherland,” which is rare in other research disciplines (including ours) in China.

Second, many Chinese astronomers would evaluate the consequences of ODS. One evaluation lens is self-interest. For example, several Chinese astronomers (e.g., P6, P12) have pointed out that ODS can potentially increase individual research outputs and their academic reputation, which motivates them to do it. It is noteworthy that some Chinese astronomers increase research outputs through ODS, both in terms of their personal contributions and for the entire astronomy community. Their evaluation priority is their own data/paper citation over ODS practices. Another evaluation lens is reciprocity. Some Chinese astronomers (e.g., P1, P10) perceive that the data sharer and user roles in ODS among astronomers can be exchanged. An open data sharer can become a user, and vice versa, in different research projects and times. As P10 mentioned, many Chinese astronomers have received the benefits of ODS from other astronomers when they lacked data or resources. As a result, they aspire to contribute to the community by providing opportunities and resources for fellow astronomers who face challenges similar to those they did previously. Thus, they adopt ODS in a respectful manner, hoping to receive the same treatment in the future. Abele-Brehm et al.’s study has revealed that researchers tended to conduct ODS out of reward promises (Abele-Brehm et al., 2019 ). Our findings complement it by differentiating self-interest-oriented and reciprocity-oriented rewards from ODS.

Third, some Chinese astronomers’ choice of ODS can be interpreted as contractual. Without ODS, they cannot receive government funding or get their research proposal accepted, which may impede their research progress and contribution. This finding corroborates Zuiderwijk and Spiers’ research, highlighting the significance of resource constraints and individual expectations benefits, which they could get extra citation or potential collaboration opportunities as essential motivators for ODS in astronomy (Zuiderwijk and Spiers, 2019 ). Furthermore, the development of modern astronomy in China is relatively retarded compared to the U.S. or European counterparts. The Chinese government sponsors most astronomical projects with public funding, hoping to enhance Chinese astronomy through centralized power and resources. For example, in 2018, the Chinese government implemented a scientific data management policy mandating the sharing of research data generated by public funding (General Office of the State Council of China, 2018 ). Thus, Chinese astronomers in contract with government-funded telescopes must enact ODS.

The societal barriers to Chinese astronomers’ ODS practices

We identified a few societal barriers to Chinese astronomers’ ODS practices. First, insufficient data rights protection during ODS may hinder Chinese astronomers’ enthusiasm or trust in conducting ODS. For example, P6 has raised the concern that some astronomical data policies are typically formulated by scientific alliances and only bind members within project teams. Thus, astronomers who do not belong to these alliances do not need to obey these policies. Moreover, P10 and P14 both complained that though they had contributed much data, time, and effort, some global ODS practices relied on verbal agreements, which often lacked enforcement and easily compromised their data rights in an international project. This insufficient protection of data rights may give rise to conflicts of interest among collaborating parties, discouraging subsequent data-sharing practices among Chinese astronomers.

Second, a data infrastructure that is weak in its usability and accessibility may deter some Chinese astronomers from choosing ODS. As P8 remarked, Chinese open research data infrastructures have not been well developed regarding data usability and accessibility, which pushes domestic astronomers to publish data via foreign open research platforms. This concern partly reflects the reality of the underdevelopment of data infrastructure in China, indicating that most of China’s domestic research data repositories have yet to establish licenses, privacy, and copyright guidelines. (Li et al., 2022 ).

Additionally, we found that a highly competitive environment could potentially trigger “Nei Juan” related to competing for publication priority, which could also affect Chinese astronomers’ ODS attitudes and behaviors. Specifically, the increasing emphasis on academic performance has led many Chinese researchers into a “weird circle” of self-imposed pressure to publish papers continuously. This phenomenon is exacerbated by the tenure system in top Chinese universities, which has significantly shaped researchers’ academic work and day-to-day practices (Xu and Poole, 2023 ). Thus, within the intensely competitive scientific landscape and the dominant evaluation system for paper publications, Chinese astronomers may potentially prioritize rapid paper publication over ODS because when scientific resources and academic promotions are scarce, data is invaluable to a researcher. As implied in P14’s quote, some Chinese astronomers may delay or opt out of ODS unless their data rights and research benefits can be ensured.

Two dimensions in the action strategies in Chinese astronomers’ choices for ODS

Apart from the individual and societal factors that motivate or deter Chinese astronomers’ OBS behaviors, we have identified two dimensions in the action strategies that influence their choice of ODS. These two dimensions are presented and interpreted in Table 3 .

First, some Chinese astronomers hesitated to ODS because they had to choose between domestic customs and international traditions in astronomy, which might influence or even determine some Chinese astronomers’ behaviors concerning ODS. For example, several Chinese astronomers (e.g., P11, P13) prioritized compliance with domestic policies over international ones in determining where and how to implement ODS (Zhang et al., 2023). Besides, as explained by P4, almost all Chinese astronomers receive national funding, which would influence their ODS behaviors due to national funding agencies’ requirements for project commitment and applications. China’s “dual track” approach emphasizing data openness and national security simultaneously requires researchers to obey the “Openness as the normal and non-openness as the exception” principle (Li et al., 2022 ). Meanwhile, open data governance and open data movement have gradually impacted government policies as various national security and personal privacy issues are emerging (Arzberger et al., 2004 ). Despite this, ODS policies or concerns about national security and personal privacy may not be suitable for astronomy because astronomy rarely involves security and privacy issues (as highlighted by P9 and P12). As the discrepancy between domestic and international policy environments widens, choosing different norms may pressure Chinese astronomers’ ODS behaviors.

Second, we found some ethical problems related to ODS from the language prerequisite or preference in Chinese astronomy. As mentioned by P12, language has become an entrance bar in Chinese astronomy because astronomy is sort of “aristocratic science” in the sense that English proficiency is a prerequisite for anyone or any institution that wants to participate in astronomy research and practices seriously. Consequently, there is no comparable citizen science project in China to Galaxy Zoo or Zooniverse in the U.S., and local or private colleges in China cannot afford to establish astronomy as a scientific discipline in their institutions because many people in Chinese citizen science projects or below-the-top institutions are not proficient in English. Related to it, as mentioned by P9, domestic journals about astronomy in China are unanimously regarded as inferior and not valuable enough for academic evaluation or promotion. This phenomenon in Chinese astronomy is distinctive from the other research disciplines in China, where domestic journals are not “biased” based on publication language.

Third, domestic astronomy projects obeying international propriety data period policies may exert extra pressure or restraint on Chinese astronomers to conduct ODS. For example, the LAMOST and FAST projects in China follow international conventions in setting their propriety data period and ODS policies in English. As a result, Chinese astronomers who are poor in English would confront logistic hindrances in harnessing these domestic astronomy projects to share their data, ideas, and publications in Chinese. If they want to implement international ODS via LAMOST or FAST, they must spend extra time, effort, or funding translating their data and ideas into English, which may affect their time and resource allocation in the other research activities within the proprietary data period, such as ODS. Hence, we surmise that this language obstacle for some Chinese astronomers could demotivate or discourage them from ODS.

Fourth, some Chinese astronomers may choose between personal development and scientific advancement regarding ODS. First, it may be due to the adverse effects of the Chinese academic promotion system on some astronomers. In China, universities and research institutions typically use publication lists to evaluate academic performance and promotion (Cyranoski, 2018 ). As P14 mentioned, competition for research publication has been growing in some areas of astronomy (e.g., burst source). Some Chinese astronomers may withhold ODS to prioritize their data rights and timely publication. It may also be interpreted by a prevalent phenomenon in the Chinese academy nowadays called “Nei Juan.” Consequently, some Chinese scholars, including astronomers, are pushed to be competitive or “selfish” to increase their research publications, citation metrics, funding opportunities, and data rights. Prior works have found that researchers’ data-sharing willingness tends to be low when perceived competition is high (Acciai et al., 2023 ; Thursby et al., 2018 ), and researchers’ intrinsic motivation gradually weakens when researchers’ organizations implement accountability measures (such as contract signing) and increasingly pursue performance-oriented academic research (Gu and Levin, 2021 ). These findings may also explain some Chinese astronomers’ hesitation about ODS.

Last but not least, astronomy is highly international, and ODS can encourage collaboration among astronomers from different countries. Nevertheless, as mentioned by P7, some collaborators may compromise their promises for data use, which disincentivizes data sharers’ willingness for continuous ODS. Astronomers, through the joint observations of multiple telescopes, can collectively identify the underlying reasons behind astronomical phenomena and thereby promote scientific advancement. However, with the impact of “Nei Juan” and the limitations of verbal commitments, some Chinese astronomers may find it challenging to choose between ODS and prioritizing their academic interests.

Conclusion and implications for future research

Many astronomers in Western countries may have taken ODS for granted to enhance astronomical discoveries and productivity. However, how strong such an assumption holds among Chinese astronomers has not been investigated or deliberated extensively. This may hinder international ODS with Chinese astronomers and lead to a misunderstanding of Chinese astronomers’ perceptions and practices of ODS. Thus, in this paper, we reported our findings from 14 semi-structured interviews and 136 open-ended survey responses with Chinese astronomers about their motivations and hesitations regarding ODS. Our study found that many Chinese astronomers regarded ODS as an international and established duty to obey or reciprocity to harness. However, some Chinese astronomers would also agonize about ODS for data rights concerns, usable and accessible data infrastructure preferences, and “Nei Juan” or academic promotion pressures. Synthesizing these findings, we summarize them as Chinese astronomers’ concerns and choices between domestic customs and international traditions in ODS. Despite the findings, our research has several limitations. First, we still need more data to test and generalize our findings about ODS to Chinese scholars in other disciplines. Second, we have not conducted a comparative analysis of perceptions, concerns, and behavioral differences among astronomers in other countries. In the future, we intend to address this gap by conducting a global study to provide a more comprehensive understanding of ODS in science.

Our research has several implications for future work. First, we advocate for empathy and compromise between domestic customs and international traditions in Chinese astronomy. Undoubtedly, developed and English-speaking countries have been dominant in science and research paradigms for a long time. On the positive side, such dominance has established various traditions, such as ODS in astronomy, which are respected and obeyed by many scholars worldwide, such as many astronomers in China. On the negative side, such long-standing scientific dominance may trigger a developing country’s domestic countermeasures or competing policies, which can agonize some domestic researchers and impede global ODS. For example, as we have revealed, some Chinese astronomers had regarded astronomy as an “aristocratic science” and screened out Chinese astronomers or citizen science participants who were not proficient in English. Future research can investigate further the power dynamics between international traditions and domestic customs in other cultures or research disciplines beyond ODS in astronomy.

Second, we suggest that the international astronomy community publish more inclusive ODS rules that consider the societal contexts of researchers from different countries with different cultural or language backgrounds. Efforts should be made to minimize the reinforcement of one’s dominant position in scientific research through ODS, and to develop more inclusive, sustainable, and equitable rules that appeal to more advantaged countries to join. This may be achieved by providing different languages of ODS platforms, translation assistance to draft collaboration agreements, and multiple options for international collaboration and communication among astronomers from different countries. In this regard, the CARE (Collective benefits, Authority to control, Responsibility, and Ethics) principles serve as a good example (Global Indigenous Data Alliance, 2019 ). Also, we propose that the Chinese government, academic institutions, and funding agencies be more globally leading and open-minded to stimulate ODS, not merely within the border but endeavor to become a global leader or at least an essential stakeholder to promote knowledge sharing and scientific collaboration.

Third, our research findings indicate that individual ethical perspectives among astronomers play a significant role in guiding their ODS practices. To start, reciprocity effectively enhances ODS regardless of the established or domestic research policies. Thus, we suggest that policymakers in China consider emphasizing more on the reciprocity benefits and build a collaborative effort across the scientific community. As the qualitative data from our findings revealed, collaboration benefits from ODS are highly motivating for Chinese astronomers. Still, we have identified concerns among Chinese astronomers. For instance, they have highlighted concerns about the limitations of verbal commitments for ODS within the proprietary data period, potentially engendering “free-riders” in research. Further, we noticed that some Chinese astronomers conduct ODS based on their respect for this tradition and obey it as their duty without considering external factors such as individual interests or community benefits. We posit that this ethical perspective is aligned with deontology. Therefore, we suggest that stakeholders of ODS, such as the scientific community, research institutions and organizations, and ODS platform developers, could propose specific norms or mottos regarding the ODS tradition in astronomy to stimulate astronomers’ voluntary sense of duty to conduct it.

Finally, since we found that some astronomers conducted ODS primarily for self-interests in academia, efforts should be made to ensure that the rights of researchers in astronomy are protected and that they do not bear any risks caused by others (e.g., data misuse, verbal breach of contract). Future research can administer surveys or experiments to explore how significantly these individual factors impact astronomers’ ODS behaviors.

Data availability

The complete translated and transcribed data from our study is available at Peking University Open Research Data ( https://doi.org/10.18170/DVN/JLJGPF ).

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Acknowledgements

The authors acknowledge the support of the Beijing Municipal Social Science Foundation under Grant [No. 22ZXC008].

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Department of Information Management, Peking University, Beijing, China

Jinya Liu & Huichuan Xia

National Science Library, Chinese Academy of Sciences, Beijing, China

Kunhua Zhao & Liping Gu

Department of Information Resource Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China

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JL: conceptualization, methodology, data collection, formal analysis, original draft, writing, and editing. KZ: review, data collection, and editing. LG: data collection; editing. HX: conceptualization; methodology; formal analysis; writing, editing, and paper finalization.

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Correspondence to Huichuan Xia .

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This study was reviewed and approved by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Sciences. All methods were carried out following the relevant guidelines and regulations. The ethical approval number of this study is H23162.

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Informed consent is a critical part of ensuring that participants are fully aware of the nature of the research and their involvement in it. Thus, our informed consent involves adequate information about the purpose of the research, methods of participant involvement, the intended use of the results, rights as a participant, and any potential risks that were provided to the participants. Before we began our interviews, we clearly explained the content of our informed consent form to our participants, provided them with ample time to read it, and thoroughly addressed any questions they had regarding the informed consent form. All participants had carefully read and agreed to an informed consent.

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Liu, J., Zhao, K., Gu, L. et al. To share or not to share, that is the question: a qualitative study of Chinese astronomers’ perceptions, practices, and hesitations about open data sharing. Humanit Soc Sci Commun 11 , 1063 (2024). https://doi.org/10.1057/s41599-024-03570-9

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Received : 16 November 2023

Accepted : 09 August 2024

Published : 22 August 2024

DOI : https://doi.org/10.1057/s41599-024-03570-9

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