the research focus meaning

Research Skills

Developing a research focus.

What is the difference between a subject and a topic ? What about between a research question and a research problem ? We often use these terms interchangeably, but they mean different things. As you begin to develop a personal research process, it is important to define these terms and be able to differentiate them. By the end of this section, you will be able to articulate a research question and develop a framework for a future study.

Topic vs. Subject

The best way to think about the difference between a topic and a subject is to think about the classes you took in high school. You took classes called “American History” and “World Literature,” but within those classes you studied more specific topics, like the Spanish-American War or The Aeneid . Academic research is similar. Your “subject” is your specialization within your major. If you are majoring in Communication Sciences and Disorders, for example, you may be most interested in the field of Audiology. Audiology is a research subject .

You wouldn’t be able to write a research paper on audiology, however. It’s far too broad; there are entire courses—and graduate degrees—for audiology. The first step in developing a research focus is to narrow your general subject to a more specific topic .

Here are some examples of how common subjects can be broken up into more specific topics:

As you can see from the chart above, topics are much more specific than subjects and they are more manageable to use when determining a research focus. A topic doesn’t give you enough to dive in and start drafting, but it is enough to help you develop a framework for turning the topic into a successful research project.

So how do you get from subject to topic ? The next section will give you some strategies.

Finding your Topic

Now that you understand the connections between your major and your discipline and how these create an academic discourse community, you are ready to begin sifting through the current topics, issues, and concerns that your discourse community is focused on at present. In academia, as elsewhere, there are trending topics. These topics reflect what people in your discipline think is most important at the moment. It might be helpful for you to consider what you have discussed in your major courses, or what you and those in your major discuss most often. What challenges do your field and its practitioners face now and in the future? When determining your topic, you will likely go through a number of steps. These will help you to sort through the many topics you will encounter and to select a topic that is relevant, current, and interesting to you. The best research topics are well defined, sufficiently narrow, and part of a larger problem in your discipline.

Identifying a topic

To select a viable topic for your research project, you should:

• Brainstorm about topics that you have encountered in your discourse community;
• Select several potential topics based on your interest(s);
• Ensure that the topic is manageable (i.e., that it is narrow enough);
• Ensure that scholarly material is available;
• Ensure that the topic is focused on a solvable problem;
• List academic terms associated with this topic;
• Use generated academic terms to search databases focused on your discipline; and
• Define your topic as a focused research question.

First, have a look at this resource that describes the rather intricate process of finding a research topic that is sufficiently narrow, yet still present enough in the literature of your discourse community to support a semester-long project:

Check your understanding

Let’s say your assignment is to research an environmental issue. This is a broad starting point, which is a normal first step.

One way to customize your topic is to consider how different disciplines approach the same topic in different ways. For example, here’s how the broad topic of “environmental issues” might be approached from different perspectives:

• Social Sciences: Economics of Using Wind to Produce Energy in the United States
• Sciences : Impact of Climate Change on the Habitat of Desert Animals in Arizona
• Arts and Humanities : Analysis of the Rhetoric of Environmental Protest Literature
• Determining your Topic. Authored by : Andrew Davis & Kerry Bowers. Provided by : University of Mississippi. Project : WRIT 250 Committee . License : CC BY-SA: Attribution-ShareAlike
• OER Commons: Begin your Research. Provided by : OER Commons. Located at : https://www.oercommons.org/courseware/module/11888/overview . License : CC BY-NC: Attribution-NonCommercial

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How to Write a Research Paper: Developing a Research Focus

• Anatomy of a Research Paper
• Developing a Research Focus
• Background Research Tips
• Searching Tips
• Scholarly Journals vs. Popular Journals
• Thesis Statement
• Annotated Bibliography
• Citing Sources
• Evaluating Sources
• Literature Review
• Academic Integrity
• Scholarship as Conversation
• Understanding Fake News
• Data, Information, Knowledge

Developing a Research Question

Developing a Strong Research Topic

Steps for Developing Your Research Focus

The ability to develop a good research topic is an important skill. An instructor may assign you a specific topic, but most often instructors require you to select your own topic of interest. When deciding on a topic, there are a few things you will need to do:

• Brainstorm for ideas.
• Choose a topic that will enable you to read and understand the articles and books you find.
• Ensure that the topic is manageable and that material is available.
• Make a list of key words.
• Be flexible. You may have to broaden or narrow your topic to fit your assignment or the sources you find.

Selecting a good topic may not be easy.  It must be narrow and focused enough to be interesting, yet broad enough to find adequate information. Before selecting your final topic, make sure you know what your final project should look like. Each class or instructor will likely require a different format or style of research project.

1. Brainstorming for a Topic

Choose a topic that interests you. Use the following questions to help generate topic ideas.

• Do you have a strong opinion on a current social or political controversy?
• Did you read or see a news story recently that has piqued your interest or made you angry or anxious?
• Do you have a personal issue, problem, or interest that you would like to know more about?
• Is there an aspect of a class that you are interested in learning more about?

Write down any key words or concepts that may be of interest to you. These terms can be helpful in your searching and used to form a more focused research topic.

Be aware of overused ideas when deciding a topic.  You may wish to avoid topics such as abortion, gun control, teen pregnancy, or suicide unless you feel you have a unique approach to the topic. Ask the instructor for ideas if you feel you are stuck or need additional guidance.

2. Read General Background Information

Read a general encyclopedia article on the top two or three topics you are considering.

Reading a broad summary enables you to get an overview of the topic and see how your idea relates to broader, narrower, and related issues. It also provides a great source for finding words commonly used to describe the topic. These keywords may be very useful to your research later.

If you can't find an article on your topic, try using broader terms and ask for help from a librarian.

The databases here is a good start to find general information. The library's print reference collection can also be useful and is located on the main floor of the library.

3. Focus Your Topic

Keep it manageable and be flexible. If you start doing more research and not finding enough sources that support your thesis, you may need to adjust your topic.

A topic will be very difficult to research if it is too broad or narrow. One way to narrow a broad topic such as "the environment" is to limit your topic.  

Some common ways to limit a topic are by:

• geographic area
• time frame:
• population group

Remember that a topic may be too difficult to research if it is too:

• locally confined - Topics this specific may only be covered in local newspapers and not in scholarly articles.
• recent - If a topic is quite recent, books or journal articles may not be available, but newspaper or magazine articles may. Also, websites related to the topic may or may not be available.
• broadly interdisciplinary - You could be overwhelmed with superficial information.
• popular - You will only find very popular articles about some topics such as sports figures and high-profile celebrities and musicians.

Putting your topic in the form of a question will help you focus on what type of information you want to collect.

If you have any difficulties or questions with focusing your topic, discuss the topic with your instructor or with a librarian.

Tips for Choosing a Topic

Can't think of a topic to research?

Interest : Choose a topic of interest to you and your reader(s); a boring topic translates into a boring paper.

Knowledge : You can be interested in a topic without knowing much about it at the beginning, but it's a good idea to learn a little about it before you begin your research. Read about the issue in a good encyclopedia or a short article to learn more, then go at it in depth. The research process mines new knowledge – you’ll learn as you go!

Breadth of Topic : How broad is the scope of your topic? Too broad a topic is unmanageable -- for example, "The Education of Children" or "The History of Books" or "Computers in Business." A topic that is too narrow and/or trivial, such as "My Favorite Pastime," is uninteresting and extremely difficult to research.

Guidelines : Carefully follow the instructor's guidelines. If none are provided in writing, ask your professor about his or her expectations. Tell your professor what you might write about and ask for feedback and advice. This should help prevent you from selecting an inappropriate topic.

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• Last Updated: Apr 4, 2024 5:51 PM
• URL: https://libguide.umary.edu/researchpaper

Airport Passenger-Related Processing Rates Guidebook (2009)

Chapter: chapter 3 - defining the research: purpose, focus, and potential uses.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

14 Chapter 3 identifies roles, relationships, and responsibilities of stakeholders. It examines principal steps involved in planning an airport passenger-rate data collection effort. It begins with the ques- tion of whether the potential benefits of the proposed effort outweigh the anticipated cost; describes different types of research (i.e., exploratory, descriptive, inferential); summarizes the questions each type addresses; and notes the ends to which the data might be used. 3.1 Roles and Responsibilities When an airport data collection event is first mentioned, it invariably raises numerous ques- tions: Who is asking for the data? How will it be used? What’s the budget? What’s the schedule? What kind of resources can be made available? Without answers to these fundamental questions, the success of your research is in jeopardy. This section will help the researcher establish the role of key stakeholders and their interrelationships within the team. Many entities can sponsor a data collection study, including airports, airlines, manufacturers, and various agencies. Likewise, there are many ways of managing and staffing the event and pro- moting involvement with stakeholders. There are therefore myriad ways of organizing a study. Exhibit 3-1 is an example of how a study could be arranged with the airport as the sponsor. 3.1.1 Client/Sponsor For airports, oversight is guided by a board, commission, or an authority consisting of appointed or elected officials. While these agencies typically provide oversight to airport man- agement and approve long-term plans and large capital expenditures, usually it is the airport director or manager who makes day-to-day decisions. Depending on the size of the airport, there may be several departments, each having its own manager. In such cases, passenger terminal-related studies would typically fall within the purview of the planning and/or engineering department and would be managed by its director. Regardless of the affiliation of the project sponsor(s), it is essential that the following ques- tions be answered clearly and unambiguously as they pertain to the sponsor at the beginning of any project: • Who has primary responsibility for defining the questions the study is intended to address? • What preference does this person or group have regarding ongoing involvement with the project? – What information would they like to receive, in what format, and with what frequency? – Who should be the principal point-of-contact (POC) on the client’s side for questions that might emerge related to the study’s focus, direction, etc.? C H A P T E R 3 Defining the Research: Purpose, Focus, and Potential Uses

Defining the Research: Purpose, Focus, and Potential Uses 15 • Who is the designated project manager, and what information would he or she like to receive, in what format, and with what frequency? • If the person given responsibility for day-to-day issues pertaining to access, authorizations, etc. is different from the project manager, who is that person, and what is the scope of issues he or she is authorized to address? • If problems or obstacles arise in implementing the study, and the project manager is not able or authorized to resolve them, what is the chain of persons through which the issue should be escalated? 3.1.2 Study Team The size of the study team will depend on the team’s depth and organization, and the size, duration, and complexity of the study itself. For a typical medium- to large-scale study, the roles listed in the following sections are the most typical. Multiple roles might be assumed by a single person or distributed across multiple persons. Titles vary as well, but the functions are largely universal. Project Manager The project manager is typically a mid-level to senior person who has the long-term, day-to- day relationship with his or her client counterpart. The need for the passenger-related process- ing rate study may initially originate from discussions between the project manager and those within the airport or airline. Survey Manager The survey manager is usually a mid-level staff person. His/her role on the project would be to oversee the day-to-day management of the data processing rate study, including leading the development of the scope, schedule, and budget; developing the team; and assigning roles and responsibilities. The survey manager would have the responsibility of ensuring the survey goals were adequately defined and met. Decision Maker Survey Manager Admin. Support Staffing Source (e.g., airport personnel, mkt. research firm) Surveyor Surveyor Surveyor Sponsor/Client (Airport) (Large Airport: Dir./Mgr.) Project Manager (Large Airport: Dir. Planning/Eng.) (Small Airport: Apt. Mgr.) Project Manager (Typ. oversees multiple tasks of which survey is but one part) Study Team (Typically, Consultant) Statistical Technical Expert Survey Assistant Data Analyst IT Analyst Other Stakeholders • Airlines • Agencies • Concessionaires Exhibit 3-1. Typical sponsor and study team roles (assuming an airport is the sponsor).

16 Airport Passenger-Related Processing Rates Guidebook Research and Statistical Expert A person(s) with expertise in research methodology and quantitative/statistical analysis should be consulted to develop, or provide comments and recommendations about, the overall methodology, the sampling plan, and so forth. Most of this person’s input would occur at the project’s initiation. A distinction is sometimes drawn in the consulting literature among differ- ent approaches to consulting. One such approach, generally referred to as process consultation might be of particular appeal.1 When acting in this role, the consultant not only provides tech- nical expertise related to the specific project, but also works with the client to develop expertise. This arrangement has the goal of, over time, reducing the reliance on the consultant. Survey Assistant The survey assistant has primary responsibility for assisting the survey project manager and secondarily to assist others on the project team throughout the duration of the study. Typically, this staff person will be at a junior level. The degree of assistance this person can provide is based on his/her level of education and current skill sets. Data Analyst The data analyst should not only be well-versed in technical analysis, but should also have a strong familiarity with the airport terminal environment. This person could be a terminal or air- port planner or aviation architect. The analyst is often largely responsible for documenting results, and responsibilities might extend to presenting findings to the client. Administrative Support Data collection efforts are inherently complex and, as such, often require a significant level of coordination and administration. The staff person serving this function would be responsible for such things as making travel plans, scheduling visits to the airport’s security office, buying supplies, shipping and receiving materials, scheduling meetings, preparing invoices and con- tracts, and editing/proofing the report. Data Collection Staff For small studies (e.g., small airports where only a few functional elements are being observed for a limited time period), airport/airline or consultant staffing may be used. For larger studies, typically examining multiple functional elements of a medium or large airport over a multi-day period, a market-research firm is frequently employed. The data collection staff reports directly to the survey manager. 3.2 Is the Study Needed? While the need for data collection is often justifiable, the benefit of validating the need, and avoiding what might be a costly, and possibly unjustified, effort well exceeds the relatively minor cost of pausing to consider a few basic questions (see Appendix C for more information). Exhibit 3-2 illustrates these questions. 3.3 Research Fundamentals This section summarizes a number of fundamental issues and terms related to the research process. (Additional detail is included in Appendix C.) 1 Schein, E. H. (1999). Process Consultation Revisited: Building the Helping Relationship. NY: Addison Wesley.

Research is a dynamic process with both deductive and inductive dimensions. This differs in some ways from what some present as the “traditional” approach to research, i.e., that theory drives hypothesis testing. Sometimes it does, but sometimes it doesn’t work this way. 3.3.1 Theory, Hypotheses, and Evidence The word “theory” often implies a formal set of laws, propositions, variables, and the like, whose relationships are clearly defined. A related implication is that theory may not be particu- larly germane to the everyday world of work. This view of theory is not incorrect, but neither is it complete. While theory can be abstract and complex in its detail, it does not necessarily have to be abstract, complex, or formal. It can be thought of more broadly and simply as an explanation of “how the world works.” For exam- ple, an organization might develop a mission or a value statement (or both); engrave the words in a medium intended to last millennia; and prominently display the statement in the workplace with the intent of communicating to all its perspective clients on issues pertinent to its view. In Defining the Research: Purpose, Focus, and Potential Uses 17 Question Things to Consider Have relevant data been collected at this airport in the past that might be used rather than collecting new data? Might you be able to get data from another airport similar in key ways to this airport? Are there data available that might help answer the research question? Might access to the data be blocked due to proprietary or security issues? Sometimes the data are perceived to be so sensitive that the “owner” of the data may not give permission to share it. Has the decision already been made, and the data are being collected to legitimize the decision? Is there anything to suggest that the study is an attempt to “prove” something true or false? What role will the results play in the decision being considered? To what extent will the decision makers be persuaded by the results? What will the decision makers accept as credible evidence? Before collecting data, make certain that the research plan will result in data that the sponsors will accept. It is better to learn beforehand, for example, that the proposed sampling plan does not meet the sponsor’s criteria for rigor. What is the cost of the potential investment that the data will help inform? What is the cost of conducting the research? Does the benefit equal or outweigh the cost? Cost should be considered not only in economic terms, but as safety, inconvenience, and so forth. Exhibit 3-2. Considerations to determine need for data collection.

2008, British Airways announced a new venture: OpenSkies. The “theory” OpenSkies used to define its clients is reflected in its advertising as shown in Exhibit 3-3. So, how does this relate to airport processing rate studies? It relates in the following two ways: 1. The published research literature may well contain formal theories relevant to what data to collect and how to collect it. For example, Appendix B includes a bibliography of recent research articles related to passenger and baggage processing in airports. It is intended to illustrate the scope and diversity of research available on a given topic. Before embarking on an investigation, review the literature to see how it might enhance the quality of the planned research. The Internet provides access to numerous sources for such scholarly documents. 2. Informally, the key decisions about how to go about collecting data are grounded in assump- tions about how things work (i.e., one’s own theory). For example, you might choose to col- lect passenger security screening data between 6:00 a.m. and 8:00 a.m. on a Monday because your experience is that this time period reflects peak checkpoint activity. While this “theory” may be correct in some circumstances, it may also be wrong in others. For example, at many vacation-oriented airports, the peak at the checkpoint occurs in the late morning due to check-out times at hotels. Another common view of research is of the stereotypical scientist, objectively testing hypothe- ses (or an “educated guess”) arising from theory. Exhibit 3-4 reflects this general approach to research. This is certainly one way in which research proceeds, but, similar to theory, it is not the only way. Before considering an “evidence first” approach, we wish to mention a variation on the tra- ditional approach displayed in Exhibit 3-4 that has been gaining dominance in recent years. In particular, this is a confidence interval (CI) approach rather than a hypothesis driven approach. In a hypothesis driven approach, the researcher’s primary interest is in testing a population parameter, and uses a sample drawn from the population. When the researcher takes a CI approach, the intent is to calculate an interval within which the population parameter is likely 18 Airport Passenger-Related Processing Rates Guidebook Exhibit 3-3. OpenSkies advertisement. Question key assumptions, even if they seem to be “common sense,” by checking with informants, look- ing at the literature, etc.

to fall. Hypotheses are stated before data collection; CIs are calculated after data are collected.2 In conducting passenger-processing rate research in airport environments, the CI approach is going to be the most appropriate in most instances. A markedly different approach to those described above is shown in Exhibit 3-5. In contrast to beginning with a theory and then collecting evidence to test the theory or estimate a popula- tion parameter within some CI, this approach begins with evidence for which one seeks poten- tial explanations, or “theories” to explain the evidence. This approach is subsumed under the broad heading of Bayesian Law, so named after the 18th Century English clergyman, Thomas Bayes, credited with developing the approach. Depending on where one begins can result in potentially dramatic conclusions (see Exhibit 3-6). This is important because limiting oneself to a particular perspective of how research should be conducted and how data ought to be gathered may impose unnecessary constraints. What is important is that the research is executed systematically and with rigor. The documented ways in which science proceeds are often idealized: portraying what is inherently a very dynamic and nonlinear process as logical and linear. 3.3.2 Research Questions and Purposes A basic issue in research is specifying the question the research will help answer. Penning a specific question also helps in determining what approach might be best used in seeking an Defining the Research: Purpose, Focus, and Potential Uses 19 Theory Drives questions & hypotheses Hypothesis: Installing n kiosks will reduce the average time of passengers waiting in line by 10% over check-in agents. Leading to a conclusion Drives data collection Followed by analysis Exhibit 3-4. Hypothesis driven approach. Evidence leads to speculation about possible explanations Which may or may not drive more data collection & analysis Theory Exhibit 3-5. Bayesian approach. 2 While these approaches are presented here as mutually exclusive, they might be integrated in practice.

answer. One classic text in research methodology5 suggests that a research question should express a relationship between two or more variables, and it should imply an empirical approach, that is, it should lend itself to being measured using data. A variable is, not surprisingly, some- thing that can vary, or assume different values. In the next section, illustrative questions are given, categorized by the purpose of research with which they are best matched. The five research purposes are presented as the following: 1. Explore, 2. Describe, 3. Test, 4. Evaluate, and 5. Predict. The distinctions among these purposes are not absolute, nor are they necessarily exclusive of one another. A research initiative might be directed at answering questions with multiple pur- poses. Indeed, this is but one of many ways of classifying research. In addition, the reader whose practice lies primarily in the arena of modeling and simulation might note their absence from this list. Although modeling and simulation applications require input data, for example, to gen- erate distributions and parameters for use as stochastic varieties in modeling, the techniques used to collect data are largely independent of specific applications (such as simulation and model- ing). Those issues unique to modeling are beyond the scope of this guidebook. Explore (Exploratory Research) Exploratory research is sometimes defined as “what to do when you don’t know what you don’t know.” Its aim is discovery and to develop an understanding of relevant variables and their interactions in a real (field) environment. Exploratory research, as such, is appropriate when the 20 Airport Passenger-Related Processing Rates Guidebook If your intent is to… And take action based on… Use… Example Test a hypothesis regarding a population parameter Whether you reject or fail to reject the null hypothesis Hypothesis testing approach The proportion of coach passengers checking in more than 60 min prior to scheduled departure is 80% H A : p > .80 3 H 0 : p .804 Estimate a population parameter The confidence interval selected CI approach Plus or minus 5%, what is the average time coach passengers check in prior to scheduled departure? Determine the likelihood of an event given some evidence The calculated probability Bayesian approach What is the probability that a passenger’s carry on- luggage will be subject to secondary security screening given that the passenger is boarding an international flight? Exhibit 3-6. Research approaches. 3 This is the research, or Alternative, hypothesis. It reads: The proportion is greater than 80%. 4 This is the null hypothesis. It is what is tested, and reads: The proportion is less than or equal to 80%. 5 Kerlinger F. & Lee, H. (2000). Foundations of Behavioral Research, 4th ed. NY: Harcourt Brace.

problem is not well defined. For example, passenger complaints about signs within a facility might prompt the following exploratory question: • “Where should signage be located to minimize passenger confusion?” As another example, if a new security checkpoint configuration is proposed, it may be too novel to rely on variables used in other studies. The question, therefore, might then be the following: • “How does a given alternative security checkpoint configuration affect capacity?” This type of research is often qualitative rather than quantitative. That is, it employs verbal descriptors of observations, rather than counts of those observations (see Appendix C for more information). Describe (Descriptive Research) Descriptive research, as the name implies, is intended to describe phenomena. While descrip- tive research might involve collecting qualitative data by asking open-ended questions in an interview, it typically employs quantitative methods resulting in reporting frequencies, calculat- ing averages, and the like. The following two questions illustrate the nature of descriptive research. Each implies that the relevant variables have been identified as well as the conditions under which the data should be collected. • “What is the average number of passengers departing on international flights on weekday evenings in July at a given airport?” • “How many men use a given restroom at a particular location at a given time?” Test (Experimental and Quasi-experimental Research and Modeling) Often, the intent of the research is not simply to describe something, but to test the impact of some intervention. In an airport environment, such research might be initiated to evaluate the relative effectiveness of a security screening technology in accurately detecting contraband. It is similar in approach to research conducted to assess the relative effectiveness of an experimental drug in comparison to a control (placebo) or another drug. Variables are often manipulated and controlled. This research lies largely outside the scope of this guidebook and, as such, will not receive much attention. Examples of questions that might be asked in this type of research include the following: • “What is the impact of posting airline personnel near check-in waiting lines on the average passenger waiting time?” In addition to the classic “experiment,” simulation modeling might be used, employing rep- resentative data to help answer questions such as the following: • “What would be the impact on processing time of a new security measure being considered?” • “How many agents are needed to keep passenger waiting time below an average of 10 min?” Evaluate (Evaluative Research) Sometimes, the intent of the research is to assess performance against some standard or stated requirement. Basically, evaluation research is concerned with seeing how well something is work- ing, with an eye toward improving performance, as illustrated by the following two questions: • “Is the performance of a given piece of equipment in the field consistent with manufacturer’s specifications?” • “On average, what proportion of passengers waits in a security checkpoint line longer than the 10-minute maximum threshold specified by an airline?” Defining the Research: Purpose, Focus, and Potential Uses 21

Predict Finally, research might be initiated to attempt to predict or anticipate potential emerging pat- terns before they occur. This is related to environmental scanning, insofar as it represents a delib- erate attempt to monitor potential trends and their impact. For example, in the early 1970s, one might have posed the following question: • “What would be the impact of an increase in the number of women in the workforce on air- port design?” There are numerous documented approaches to answering questions such as these. While well beyond the scope of this guidebook, here is one as illustrative: scenario planning. This method involves convening persons with relevant expertise to identify those areas that might most impact the industry (e.g., regulation, fuel costs, demographic changes), and then to systemati- cally consider what the best, worst, and might likely scenarios might be. The principal value of such an approach is that it facilitates deliberate consideration of future trends, and in so doing, presumably leaves people better prepared. When the goal of the research is to predict, data from multiple sources might be sought. The scenario planning example relies, to an extent, on the judgments of experts. Probabilities can also be drawn from historical data to help identify patterns and trends. Exhibit 3-7 is a summary of the key characteristics of each research type. 3.4 Developing the Research Plan Large research studies, particularly when funding is being requested, often require the researchers to adhere to a specific set of technical requirements. The Research Team is aware that the ad hoc and short timeline of many airport-planning research efforts makes developing a “for- mal” research plan impracticable. Nonetheless, even though you might not have the “luxury” of 22 Airport Passenger-Related Processing Rates Guidebook Research Purpose Characteristics Explore Primary purpose: to better define or understand a situation. Data will help answer the research question. The benefit of conducting the research justifies the cost. Qualitative data are recorded, using observation. Describe Primary purpose: to provide descriptive information about something. Test Primary purpose: to assess the impact of a proposed change in procedure or policy. Evaluate Primary purpose: to assess performance against requirements. Predict Primary purpose: to consider possible future circumstances with the purpose of being better prepared for emerging trends. Exhibit 3-7. Summary of research types.

developing such a plan, there are benefits to considering the issues described in this section, as well as documenting basic information. The following are the three major elements the Research Team believes worth documenting, regardless of the size of the research endeavor.6 1. Goals or aims. 2. Background and significance. 3. Research design and methods. Each is described in the sections that follow. 3.4.1 Goals or Aims Specify the question the research is intended to help answer or the specific purpose of the research. The experience of having to translate an intended purpose into words can help clarify your intent. In addition, a written statement can serve as a way of ensuring that your understand- ing of the purpose of the research is consistent with that of the sponsor and other stakeholders. Two examples follow: Statement of Purpose—Example 1 The purpose of this study is to aid decision makers in determining if extending the dwell time of the airport’s automated guideway transit system (AGTS) vehicles from 30 sec to 35 sec at the Concourse C station might improve overall system capacity by providing more boarding time for passengers. Statement of Purpose—Example 2 The goal of this study is to provide airport management with recent data showing the percent- age of arriving flights whose first checked bag reaches the claim device within the airport’s goal of 15 min. 3.4.2 Background and Significance Document what is already known, and specify how the proposed research initiative will add to this knowledge. Consider a “devil’s advocate” perspective by asking what the consequences of not doing the research might be. 3.4.3 Research Design and Methods In this section, describe how you will go about collecting and analyzing data. Additional infor- mation about these issues, including sampling strategies and sample size, is presented in Chapter 5 and in Appendix C. The research plan does not need be lengthy. It should, however, capture key information that, were it not documented and those familiar with the research were not available, would be diffi- cult to ascertain. Defining the Research: Purpose, Focus, and Potential Uses 23 6 This section is partly based on guidelines published by the Agency for Healthcare Research and Quality, Department of Health and Human Services. http://www.ahrq.gov/fund/esstplan.htm.

TRB’s Airport Cooperative Research Program (ACRP) Report 23: Airport Passenger-Related Processing Rates Guidebook provides guidance on how to collect accurate passenger-related processing data for evaluating facility requirements to promote efficient and cost-effective airport terminal design.

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• University Libraries Databases
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• Develop a Research Focus
• Library Resources to Start With

Forming a Focus

Exploring information.

• Advanced Search Techniques
• Collecting Information

After seeing some directions that your topic can take, you need to find your research focus. Visualize your topic as branching arrows with the different subsections of your topic flowing out from your topic. Your research focus can be one of these arrows (aspect) or a grouping of arrows (theme).

Some topics can be broken down into many individual parts and while they are all related, one part stands out to you. It can be discussed on its own and has enough resources available for your project. This can be a specific instance of your broad topic. For example, if your topic is LGBTQIA2S+ representation in media, an aspect would be a specific character.

Other topics have individual parts that are best discussed in small groups. There's an overarching theme that ties them all together. While this is more specific than the broad topic, it still has a few separate ideas. The theme approach is best used when each individual aspect of a topic does not have enough information on its own. For example, if your topic is social media influencers, a theme would be authenticity which can be broken down into self-branding, ethics, and self-image.

Now that you have a research topic, you need to find a focus. Think back on the questions you answered for the topic you choose:

• What words are being used in titles and abstracts (article summaries) to describe the topic?
• Are there names, dates, places, things, etc. that are repeatedly mentioned?
• Is there anything more specific about a topic that sounds interesting?

The words, names, dates, places, things, etc. you found earlier are all threads that are a part of your broad topic. Search using those terms in Google , Library Search , and Google Scholar . Are you finding anything different or more specific about a certain aspect of your topic?

Exploring in Library Search

In Library Search, scroll down to find the Subjects listed on an Item Record. These are terms you can use to search for similar sources. They can also narrow your focus.

In Library Search, some resources have Related Reading on the right side of the Item Record. These are recommended items with similar topics or are a more specific aspect of your topic. Sometimes these Related Reading lists have what you were looking for even if it was not including in the search results.

Exploring in Google Scholar

Similar to Related Reading in Library Search, Google Scholar has a Related Articles link on most items to find similar sources.

• << Previous: Library Resources to Start With
• Next: Advanced Search Techniques >>

Research Focus vs Thesis/Argument

An argument or thesis statement is best developed closer to when you begin to write your paper or create your project. At that point, you have found most of the available information on your topic and can form a solid, unchanging opinion or idea around it.

A research focus helps guide your research just as a thesis statement guides your writing but a research focus changes and evolves as you encounter new information.

• Last Updated: Mar 21, 2024 10:52 AM
• URL: https://libguides.wmich.edu/philosophy

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You are here, developing your central research focus.

In order to identify a relevant and useful research question it is first necessary to define an initial research focus. It is essential to select an area of research that interests you as this will help to maintain your motivation, in what is a long and rigorous process. In addition the relevance of the research focus needs to be considered in relation to how it links to current policies, research and developments in education (Menter, Elliot, Hulme, Lewoin, and Lowden, 2011). The feasibility of the project relates to the timeline for the research and researcher expertise in developing and using the chosen methods. In addition it is necessary to consider the study population, i.e. where or from whom you plan to obtain your data to enable you to select the most appropriate groups or contexts for answering your research questions and to ensure that there is sufficient accessibility for you to carry out your research (Kumar 2011).

Key things to consider:

Which aspects of education would you be interested in researching? It is very important for you to select an area of research that you are interested in as the research process is very intensive and you will be far more motivated to research an area of interest.

Are there aspects of your own practice or of the educational setting in which you work which you would like to investigate as a precursor to implementing change? This can be a rewarding field as research as findings can directly influence practice, however it is necessary to ensure that any area you choose an area that where there is existing research for you to build on.

Some features which characterise the early stages of a successful research project are listed below (Campbell 1982, cited in Robson, 2000):

The research arises out of a real world problem.

The researcher develops a good understanding of relevant theoretical perspectives by reading literature focussing on theory and research in the area of interest.

Well-developed contacts are developed with professionals within that field of study.

A framework devised by Cresswell (2011) as a template for structuring the development of a research problem has been adapted below to help you identify areas that you need to consider when developing your central research focus:

Topic: general statement of the area to be researched

Research problem: an issue within that research area which could form the basis of research

Justification for the research problem: evidence of some form which identifies the issue as being one which would benefit from further exploration e.g. deficiencies in existing research; personal observations; research findings.

Relating the discussion to audiences: explicit identification of audiences who would benefit from the research or find it of interest.

You may find it helpful to use the questions below which are based on Cresswell’s framework to help you evaluate your ideas for possible central research questions:

How could you justify researching this issue?

What evidence do you have for your justification?

Who would be interested in / benefit from this research?

Often it is necessary to have some form of stimulus to help you develop your initial ideas about what you want to research. Different starting points for research from which it is possible to develop your research focus include:

published research which focuses on effective practice

reflections on personal practical experiences and observations

educational theories

contemporary issues of significance in development of policy, exploring the possible impact of policy decisions on practice

responding to stakeholder needs e.g. those of a particular group of pupils.

Identifying issues to research

Identify some issues within education and write three questions in relation to this that could be a starting point for research. You can get ideas from this from your educational setting or from current issues, for example see resources below:

Websites for organisations which fund research projects:

Economic and Social Research Council

http://www.esrc.ac.uk

The Nuffield Foundation

http://www.nuffieldfoundation.org/

The Leverhulme Trust

http://www.leverhulme.ac.uk/

The Times Educational Supplement and Times Higher Education Supplement

Evaluate the questions you have identified and choose a question to develop further and carry out some reading on related research. Rework your question based on the ideas from your readings. How has your initial question changed?

• Research Design

Creative Commons

Focusing a Research Topic

Ask these questions:

What is it?

Why should I do it?

How do I do it?

For example:   Say you have to do a research project about World War II, and you don't know a thing about it, nor are you at all interested in it. Try to find a subtopic that connects to your interests.   If you like cars, try comparing the land vehicles used by the Germans and the Americans. If you like fashion, look at women's fashions during the war and how they were influenced by military uniforms and the shortage of certain materials. If you like animals, look at the use of dogs by the US Armed Forces. If you like puzzles and brain teasers, look at the fascinating topic of decoding secret messages. If you like music, find out what types of music American teenagers were listening to during the war years. If you are a pacifist, find out what the anti-war movement was like during the war in any country. Find out what was happening during the war on your birth date. Find out if any of your relatives fought in the war and research that time and place.

TODAY'S HOURS:

Research Process

• Select a Topic
• Find Background Info

Focus Your Topic

• List Keywords
• Search for Sources
• Evaluate & Integrate Sources
• Cite and Track Sources
• Scientific Research & Study Design

Related Guides

• Research Topic Ideas by Liz Svoboda Last Updated May 30, 2024 150135 views this year
• Identifying Information Sources by Liz Svoboda Last Updated Mar 13, 2024 2600 views this year
• Understanding Journals: Peer-Reviewed, Scholarly, & Popular by Liz Svoboda Last Updated Jan 10, 2024 1606 views this year

Have a Question? Need Some Help?

Email: [email protected] Phone: (810) 762-3400 Text message: (810) 407-5434 (text messages only)

Keep it manageable and be flexible. If you start doing more research and not finding enough sources that support your thesis, you may need to adjust your topic.

A topic will be very difficult to research if it is too broad or narrow. One way to narrow a broad topic such as "the environment" is to limit your topic. Some common ways to limit a topic are:

• by geographic area

Example: What environmental issues are most important in the Southwestern United States?

Example: How does the environment fit into the Navajo world view?

• by time frame

Example: What are the most prominent environmental issues of the last 10 years?

• by discipline

Example: How does environmental awareness effect business practices today?

• by population group

Example: What are the effects of air pollution on senior citizens?

Remember that a topic may be too difficult to research if it is too:

• locally confined - Topics this specific may only be covered in local newspapers and not in scholarly articles.

Example: What sources of pollution affect the air in Genesee County?

• recent - If a topic is quite recent, books or journal articles may not be available, but newspaper or magazine articles may. Also, websites related to the topic may or may not be available.
• broadly interdisciplinary - You could be overwhelmed with superficial information.

Example: How can the environment contribute to the culture, politics and society of the Western United States?

• popular - You will only find very popular articles about some topics such as sports figures and high-profile celebrities and musicians.

Putting your topic in the form of a question will help you focus on what type of information you want to collect.

If you have any difficulties or questions with focusing your topic, discuss the topic with your instructor, or with a librarian.

• Topic Concept Map Download and print this PDF to create a concept map for your topic. Put your main topic in the middle circle and then put ideas related to your topic on the lines radiating from the circle.
• << Previous: Find Background Info
• Next: List Keywords >>
• Last Updated: May 29, 2024 1:31 PM
• URL: https://libguides.umflint.edu/research

What Is a Focus Group?

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.

A focus group is a qualitative research method that involves facilitating a small group discussion with participants who share common characteristics or experiences that are relevant to the research topic. The goal is to gain insights through group conversation and observation of dynamics.

In a focus group:

• A moderator asks questions and leads a group of typically 6 to 12 pre-screened participants through a discussion focused on a particular topic.
• Group members are encouraged to talk with one another, exchange anecdotes, comment on each others’ experiences and points of view, and build on each others’ responses.
• The goal is to create a candid, natural conversation that provides insights into the participants’ perceptions, attitudes, beliefs, and opinions on the topic.
• Focus groups capitalize on group dynamics to elicit multiple perspectives in a social environment as participants are influenced by and influence others through open discussion.
• The interactive responses allow researchers to quickly gather more contextual, nuanced qualitative data compared to surveys or one-on-one interviews.

Focus groups allow researchers to gather perspectives from multiple people at once in an interactive group setting. This group dynamic surfaces richer responses as participants build on each other’s comments, discuss issues in-depth, and voice agreements or disagreements.

It is important that participants feel comfortable expressing diverse viewpoints rather than being pressured into a consensus.

Focus groups emerged as an alternative to questionnaires in the 1930s over concerns that surveys fostered passive responses or failed to capture people’s authentic perspectives.

During World War II, focus groups were used to evaluate military morale-boosting radio programs. By the 1950s focus groups became widely adopted in marketing research to test consumer preferences.

A key benefit K. Merton highlighted in 1956 was grouping participants with shared knowledge of a topic. This common grounding enables people to provide context to their experiences and allows contrasts between viewpoints to emerge across the group.

As a result, focus groups can elicit a wider range of perspectives than one-on-one interviews.

Step 1 : Clarify the Focus Group’s Purpose and Orientation

Clarify the purpose and orientation of the focus group (Tracy, 2013). Carefully consider whether a focus group or individual interviews will provide the type of qualitative data needed to address your research questions.

Determine if the interactive, fast-paced group discussion format is aligned with gathering perspectives vs. in-depth attitudes on a topic.

Consider incorporating special techniques like extended focus groups with pre-surveys, touchstones using creative imagery/metaphors to focus the topic, or bracketing through ongoing conceptual inspection.

For example

A touchstone in a focus group refers to using a shared experience, activity, metaphor, or other creative technique to provide a common reference point and orientation for grounding the discussion.

The purpose of Mulvale et al. (2021) was to understand the hospital experiences of youth after suicide attempts.

The researchers created a touchstone to focus the discussion specifically around the hospital visit. This provided a shared orientation for the vulnerable participants to open up about their emotional journeys.

In the example from Mulvale et al. (2021), the researchers designated the hospital visit following suicide attempts as the touchstone. This means:

• The visit served as a defining shared experience all youth participants could draw upon to guide the focus group discussion, since they unfortunately had this in common.
• Framing questions around recounting and making meaning out of the hospitalization focused the conversation to elicit rich details about interactions, emotions, challenges, supports needed, and more in relation to this watershed event.
• The hospital visit as a touchstone likely resonated profoundly across youth given the intensity and vulnerability surrounding their suicide attempts. This deepened their willingness to open up and established group rapport.

So in this case, the touchstone concentrated the dialogue around a common catalyst experience enabling youth to build understanding, voice difficulties, and potentially find healing through sharing their journey with empathetic peers who had endured the same trauma.

Step 2 : Select a Homogeneous Grouping Characteristic

Select a homogeneous grouping characteristic (Krueger & Casey, 2009) to recruit participants with a commonality, like shared roles, experiences, or demographics, to enable meaningful discussion.

A sample size of between 6 to 10 participants allows for adequate mingling (MacIntosh 1993).

More members may diminish the ability to capture all viewpoints. Fewer risks limited diversity of thought.

Balance recruitment across income, gender, age, and cultural factors to increase heterogeneity in perspectives. Consider screening criteria to qualify relevant participants.

Choosing focus group participants requires balancing homogeneity and diversity – too much variation across gender, class, profession, etc., can inhibit sharing, while over-similarity limits perspectives. Groups should feel mutual comfort and relevance of experience to enable open contributions while still representing a mix of viewpoints on the topic (Morgan 1988).

Mulvale et al. (2021) determined grouping by gender rather than age or ethnicity was more impactful for suicide attempt experiences.

They fostered difficult discussions by bringing together male and female youth separately based on the sensitive nature of topics like societal expectations around distress.

Step 3 : Designate a Moderator

Designate a skilled, neutral moderator (Crowe, 2003; Morgan, 1997) to steer productive dialogue given their expertise in guiding group interactions. Consider cultural insider moderators positioned to foster participant sharing by understanding community norms.

Define moderator responsibilities like directing discussion flow, monitoring air time across members, and capturing observational notes on behaviors/dynamics.

Choose whether the moderator also analyzes data or only facilitates the group.

Mulvale et al. (2021) designated a moderator experienced working with marginalized youth to encourage sharing by establishing an empathetic, non-judgmental environment through trust-building and active listening guidance.

Step 4 : Develop a Focus Group Guide

Develop an extensive focus group guide (Krueger & Casey, 2009). Include an introduction to set a relaxed tone, explain the study rationale, review confidentiality protection procedures, and facilitate a participant introduction activity.

Also include guidelines reiterating respect, listening, and sharing principles both verbally and in writing.

Group confidentiality agreement

The group context introduces distinct ethical demands around informed consent, participant expectations, confidentiality, and data treatment. Establishing guidelines at the outset helps address relevant issues.

Create a group confidentiality agreement (Berg, 2004) specifying that all comments made during the session must remain private, anonymous in data analysis, and not discussed outside the group without permission.

Have it signed, demonstrating a communal commitment to sustaining a safe, secure environment for honest sharing.

Berg (2004) recommends a formal signed agreement prohibiting participants from publicly talking about anything said in the focus group without permission. This reassures members their personal disclosures are safeguarded.

Develop questions starting general then funneling down to 10-12 key questions on critical topics. Integrate think/pair/share activities between question sets to encourage inclusion. Close with a conclusion to summarize key ideas voiced without endorsing consensus.

Krueger and Casey (2009) recommend structuring focus group questions in five stages:

Opening Questions:

• Start with easy, non-threatening questions to make participants comfortable, often related to their background and experience with the topic.
• Get everyone talking and open up initial dialogue.
• Example: “Let’s go around and have each person share how long you’ve lived in this city.”

Introductory Questions:

• Transition to the key focus group objectives and main topics of interest.
• Remain quite general to provide baseline understanding before drilling down.
• Example: “Thinking broadly, how would you describe the arts and cultural offerings in your community?”

Transition Questions:

• Serve as a logical link between introductory and key questions.
• Funnel participants toward critical topics guided by research aims.
• Example: “Specifically related to concerts and theatre performances, what venues in town have you attended events at over the past year?”

Key Questions:

• Drive at the heart of study goals, and issues under investigation.
• Ask 5-10 questions that foster organic, interactive discussion between participants.
• Example: “What enhances or detracts from the concert-going experience at these various venues?”

Ending Questions:

• Provide an opportunity for final thoughts or anything missed.
• Assess the degree of consensus on key topics.
• Example: “If you could improve just one thing about the concert and theatre options here, what would you prioritize?”

It is vital to extensively pilot test draft questions to hone the wording, flow, timing, tone and tackle any gaps to adequately cover research objectives through dynamic group discussion.

Step 5 : Prepare the focus group room

Prepare the focus group room (Krueger & Casey, 2009) attending to details like circular seating for eye contact, centralized recording equipment with backup power, name cards, and refreshments to create a welcoming, affirming environment critical for participants to feel valued, comfortable engaging in genuine dialogue as a collective.

Arrange seating comfortably in a circle to facilitate discussion flow and eye contact among members. Decide if space for breakout conversations or activities like role-playing is needed.

Refreshments

• Coordinate snacks or light refreshments to be available when focus group members arrive, especially for longer sessions. This contributes to a welcoming atmosphere.
• Even if no snacks are provided, consider making bottled water available throughout the session.
• Set out colorful pens and blank name tags for focus group members to write their preferred name or pseudonym when they arrive.
• Attaching name tags to clothing facilitates interaction and expedites learning names.
• If short on preparation time, prepare printed name tags in advance based on RSVPs, but blank name tags enable anonymity if preferred.

Krueger & Casey (2009) suggest welcoming focus group members with comfortable, inclusive seating arrangements in a circle to enable eye contact. Providing snacks and music sets a relaxed tone.

Step 6 : Conduct the focus group

Conduct the focus group utilizing moderation skills like conveying empathy, observing verbal and non-verbal cues, gently redirecting and probing overlooked members, and affirming the usefulness of knowledge sharing.

Use facilitation principles (Krueger & Casey, 2009; Tracy 2013) like ensuring psychological safety, mutual respect, equitable airtime, and eliciting an array of perspectives to expand group knowledge. Gain member buy-in through collaborative review.

Record discussions through detailed note-taking, audio/video recording, and seating charts tracking engaged participation.

The role of moderator

The moderator is critical in facilitating open, interactive discussion in the group. Their main responsibilities are:

• Providing clear explanations of the purpose and helping participants feel comfortable
• Promoting debate by asking open-ended questions
• Drawing out differences of opinion and a range of perspectives by challenging participants
• Probing for more details when needed or moving the conversation forward
• Keeping the discussion focused and on track
• Ensuring all participants get a chance to speak
• Remaining neutral and non-judgmental, without sharing personal opinions

Moderators need strong interpersonal abilities to build participant trust and comfort sharing. The degree of control and input from the moderator depends on the research goals and personal style.

With multiple moderators, roles, and responsibilities should be clear and consistent across groups. Careful preparation is key for effective moderation.

Mulvale et al. (2021) fostered psychological safety for youth to share intense emotions about suicide attempts without judgment. The moderator ensured equitable speaking opportunities within a compassionate climate.

Krueger & Casey (2009) advise moderators to handle displays of distress empathetically by offering a break and emotional support through active listening instead of ignoring reactions. This upholds ethical principles.

Advantages and disadvantages of focus groups

Focus groups efficiently provide interactive qualitative data that can yield useful insights into emerging themes. However, findings may be skewed by group behaviors and still require larger sample validation through added research methods. Careful planning is vital.

• Efficient way to gather a range of perspectives in participants’ own words in a short time
• Group dynamic encourages more complex responses as members build on others’ comments
• Can observe meaningful group interactions, consensus, or disagreements
• Flexibility for moderators to probe unanticipated insights during discussion
• Often feels more comfortable sharing as part of a group rather than one-on-one
• Helps participants recall and reflect by listening to others tell their stories

Disadvantages

• Small sample size makes findings difficult to generalize
• Groupthink: influential members may discourage dissenting views from being shared
• Social desirability bias: reluctance from participants to oppose perceived majority opinions
• Requires highly skilled moderators to foster inclusive participation and contain domineering members
• Confidentiality harder to ensure than with individual interviews
• Transcriptions may have overlapping talk that is difficult to capture accurately
• Group dynamics adds layers of complexity for analysis beyond just the content of responses

Goss, J. D., & Leinbach, T. R. (1996). Focus groups as alternative research practice: experience with transmigrants in Indonesia.  Area , 115-123.

Kitzinger, J. (1994). The methodology of focus groups: the importance of interaction between research participants .  Sociology of health & illness ,  16 (1), 103-121.

Kitzinger J. (1995). Introducing focus groups. British Medical Journal, 311 , 299-302.

Morgan D.L. (1988). Focus groups as qualitative research . London: Sage.

Mulvale, G., Green, J., Miatello, A., Cassidy, A. E., & Martens, T. (2021). Finding harmony within dissonance: engaging patients, family/caregivers and service providers in research to fundamentally restructure relationships through integrative dynamics .  Health Expectations ,  24 , 147-160.

Powell, R. A., Single, H. M., & Lloyd, K. R. (1996). Focus groups in mental health research: enhancing the validity of user and provider questionnaires .  International Journal of Social Psychiatry ,  42 (3), 193-206.

Puchta, C., & Potter, J. (2004). Focus group practice . Sage.

Redmond, R. A., & Curtis, E. A. (2009). Focus groups: principles and process.  Nurse researcher ,  16 (3).

Smith, J. A., Scammon, D. L., & Beck, S. L. (1995). Using patient focus groups for new patient services.  The Joint Commission Journal on Quality Improvement ,  21 (1), 22-31.

Smithson, J. (2008). Focus groups.  The Sage handbook of social research methods , 357-370.

White, G. E., & Thomson, A. N. (1995). Anonymized focus groups as a research tool for health professionals.  Qualitative Health Research ,  5 (2), 256-261.

Download PDF slides of the presentation ‘ Conducting Focus Groups – A Brief Overview ‘

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Chapter 12. Focus Groups

Introduction.

Focus groups are a particular and special form of interviewing in which the interview asks focused questions of a group of persons, optimally between five and eight. This group can be close friends, family members, or complete strangers. They can have a lot in common or nothing in common. Unlike one-on-one interviews, which can probe deeply, focus group questions are narrowly tailored (“focused”) to a particular topic and issue and, with notable exceptions, operate at the shallow end of inquiry. For example, market researchers use focus groups to find out why groups of people choose one brand of product over another. Because focus groups are often used for commercial purposes, they sometimes have a bit of a stigma among researchers. This is unfortunate, as the focus group is a helpful addition to the qualitative researcher’s toolkit. Focus groups explicitly use group interaction to assist in the data collection. They are particularly useful as supplements to one-on-one interviews or in data triangulation. They are sometimes used to initiate areas of inquiry for later data collection methods. This chapter describes the main forms of focus groups, lays out some key differences among those forms, and provides guidance on how to manage focus group interviews.

Focus Groups: What Are They and When to Use Them

As interviews, focus groups can be helpfully distinguished from one-on-one interviews. The purpose of conducting a focus group is not to expand the number of people one interviews: the focus group is a different entity entirely. The focus is on the group and its interactions and evaluations rather than on the individuals in that group. If you want to know how individuals understand their lives and their individual experiences, it is best to ask them individually. If you want to find out how a group forms a collective opinion about something (whether a product or an event or an experience), then conducting a focus group is preferable. The power of focus groups resides in their being both focused and oriented to the group . They are best used when you are interested in the shared meanings of a group or how people discuss a topic publicly or when you want to observe the social formation of evaluations. The interaction of the group members is an asset in this method of data collection. If your questions would not benefit from group interaction, this is a good indicator that you should probably use individual interviews (chapter 11). Avoid using focus groups when you are interested in personal information or strive to uncover deeply buried beliefs or personal narratives. In general, you want to avoid using focus groups when the subject matter is polarizing, as people are less likely to be honest in a group setting. There are a few exceptions, such as when you are conducting focus groups with people who are not strangers and/or you are attempting to probe deeply into group beliefs and evaluations. But caution is warranted in these cases. [1]

As with interviewing in general, there are many forms of focus groups. Focus groups are widely used by nonresearchers, so it is important to distinguish these uses from the research focus group. Businesses routinely employ marketing focus groups to test out products or campaigns. Jury consultants employ “mock” jury focus groups, testing out legal case strategies in advance of actual trials. Organizations of various kinds use focus group interviews for program evaluation (e.g., to gauge the effectiveness of a diversity training workshop). The research focus group has many similarities with all these uses but is specifically tailored to a research (rather than applied) interest. The line between application and research use can be blurry, however. To take the case of evaluating the effectiveness of a diversity training workshop, the same interviewer may be conducting focus group interviews both to provide specific actionable feedback for the workshop leaders (this is the application aspect) and to learn more about how people respond to diversity training (an interesting research question with theoretically generalizable results).

When forming a focus group, there are two different strategies for inclusion. Diversity focus groups include people with diverse perspectives and experiences. This helps the researcher identify commonalities across this diversity and/or note interactions across differences. What kind of diversity to capture depends on the research question, but care should be taken to ensure that those participating are not set up for attack from other participants. This is why many warn against diversity focus groups, especially around politically sensitive topics. The other strategy is to build a convergence focus group , which includes people with similar perspectives and experiences. These are particularly helpful for identifying shared patterns and group consensus. The important thing is to closely consider who will be invited to participate and what the composition of the group will be in advance. Some review of sampling techniques (see chapter 5) may be helpful here.

Moderating a focus group can be a challenge (more on this below). For this reason, confining your group to no more than eight participants is recommended. You probably want at least four persons to capture group interaction. Fewer than four participants can also make it more difficult for participants to remain (relatively) anonymous—there is less of a group in which to hide. There are exceptions to these recommendations. You might want to conduct a focus group with a naturally occurring group, as in the case of a family of three, a social club of ten, or a program of fifteen. When the persons know one another, the problems of too few for anonymity don’t apply, and although ten to fifteen can be unwieldy to manage, there are strategies to make this possible. If you really are interested in this group’s dynamic (not just a set of random strangers’ dynamic), then you will want to include all its members or as many as are willing and able to participate.

There are many benefits to conducting focus groups, the first of which is their interactivity. Participants can make comparisons, can elaborate on what has been voiced by another, and can even check one another, leading to real-time reevaluations. This last benefit is one reason they are sometimes employed specifically for consciousness raising or building group cohesion. This form of data collection has an activist application when done carefully and appropriately. It can be fun, especially for the participants. Additionally, what does not come up in a focus group, especially when expected by the researcher, can be very illuminating.

Many of these benefits do incur costs, however. The multiplicity of voices in a good focus group interview can be overwhelming both to moderate and later to transcribe. Because of the focused nature, deep probing is not possible (or desirable). You might only get superficial thinking or what people are willing to put out there publicly. If that is what you are interested in, good. If you want deeper insight, you probably will not get that here. Relatedly, extreme views are often suppressed, and marginal viewpoints are unspoken or, if spoken, derided. You will get the majority group consensus and very little of minority viewpoints. Because people will be engaged with one another, there is the possibility of cut-off sentences, making it even more likely to hear broad brush themes and not detailed specifics. There really is very little opportunity for specific follow-up questions to individuals. Reading over a transcript, you may be frustrated by avenues of inquiry that were foreclosed early.

Some people expect that conducting focus groups is an efficient form of data collection. After all, you get to hear from eight people instead of just one in the same amount of time! But this is a serious misunderstanding. What you hear in a focus group is one single group interview or discussion. It is not the same thing at all as conducting eight single one-hour interviews. Each focus group counts as “one.” Most likely, you will need to conduct several focus groups, and you can design these as comparisons to one another. For example, the American Sociological Association (ASA) Task Force on First-Generation and Working-Class Persons in Sociology began its study of the impact of class in sociology by conducting five separate focus groups with different groups of sociologists: graduate students, faculty (in general), community college faculty, faculty of color, and a racially diverse group of students and faculty. Even though the total number of participants was close to forty, the “number” of cases was five. It is highly recommended that when employing focus groups, you plan on composing more than one and at least three. This allows you to take note of and potentially discount findings from a group with idiosyncratic dynamics, such as where a particularly dominant personality silences all other voices. In other words, putting all your eggs into a single focus group basket is not a good idea.

How to Conduct a Focus Group Interview/Discussion

Advance preparations.

Once you have selected your focus groups and set a date and time, there are a few things you will want to plan out before meeting.

As with interviews, you begin by creating an interview (or discussion) guide. Where a good one-on-one interview guide should include ten to twelve main topics with possible prompts and follow-ups (see the example provided in chapter 11), the focus group guide should be more narrowly tailored to a single focus or topic area. For example, a focus might be “How students coped with online learning during the pandemic,” and a series of possible questions would be drafted that would help prod participants to think about and discuss this topic. These questions or discussion prompts can be creative and may include stimulus materials (watching a video or hearing a story) or posing hypotheticals. For example, Cech ( 2021 ) has a great hypothetical, asking what a fictional character should do: keep his boring job in computers or follow his passion and open a restaurant. You can ask a focus group this question and see what results—how the group comes to define a “good job,” what questions they ask about the hypothetical (How boring is his job really? Does he hate getting up in the morning, or is it more of an everyday tedium? What kind of financial support will he have if he quits? Does he even know how to run a restaurant?), and how they reach a consensus or create clear patterns of disagreement are all interesting findings that can be generated through this technique.

As with the above example (“What should Joe do?”), it is best to keep the questions you ask simple and easily understood by everyone. Thinking about the sequence of the questions/prompts is important, just as it is in conducting any interviews.

Avoid embarrassing questions. Always leave an out for the “I have a friend who X” response rather than pushing people to divulge personal information. Asking “How do you think students coped?” is better than “How did you cope?” Chances are, some participants will begin talking about themselves without you directly asking them to do so, but allowing impersonal responses here is good. The group itself will determine how deep and how personal it wants to go. This is not the time or place to push anyone out of their comfort zone!

Of course, people have different levels of comfort talking publicly about certain topics. You will have provided detailed information to your focus group participants beforehand and secured consent. But even so, the conversation may take a turn that makes someone uncomfortable. Be on the lookout for this, and remind everyone of their ability to opt out—to stay silent or to leave if necessary. Rather than call attention to anyone in this way, you also want to let everyone know they are free to walk around—to get up and get coffee (more on this below) or use the restroom or just step out of the room to take a call. Of course, you don’t really want anyone to do any of these things, and chances are everyone will stay seated during the hour, but you should leave this “out” for those who need it.

Have copies of consent forms and any supplemental questionnaire (e.g., demographic information) you are using prepared in advance. Ask a friend or colleague to assist you on the day of the focus group. They can be responsible for making sure the recording equipment is functioning and may even take some notes on body language while you are moderating the discussion. Order food (coffee or snacks) for the group. This is important! Having refreshments will be appreciated by your participants and really damps down the anxiety level. Bring name tags and pens. Find a quiet welcoming space to convene. Often this is a classroom where you move chairs into a circle, but public libraries often have meeting rooms that are ideal places for community members to meet. Be sure that the space allows for food.

Researcher Note

When I was designing my research plan for studying activist groups, I consulted one of the best qualitative researchers I knew, my late friend Raphael Ezekiel, author of The Racist Mind . He looked at my plan to hand people demographic surveys at the end of the meetings I planned to observe and said, “This methodology is missing one crucial thing.” “What?” I asked breathlessly, anticipating some technical insider tip. “Chocolate!” he answered. “They’ll be tired, ready to leave when you ask them to fill something out. Offer an incentive, and they will stick around.” It worked! As the meetings began to wind down, I would whip some bags of chocolate candies out of my bag. Everyone would stare, and I’d say they were my thank-you gift to anyone who filled out my survey. Once I learned to include some sugar-free candies for diabetics, my typical response rate was 100 percent. (And it gave me an additional class-culture data point by noticing who chose which brand; sure enough, Lindt balls went faster at majority professional-middle-class groups, and Hershey’s minibars went faster at majority working-class groups.)

—Betsy Leondar-Wright, author of Missing Class , coauthor of The Color of Wealth , associate professor of sociology at Lasell University, and coordinator of staffing at the Mission Project for Class Action

During the Focus Group

As people arrive, greet them warmly, and make sure you get a signed consent form (if not in advance). If you are using name tags, ask them to fill one out and wear it. Let them get food and find a seat and do a little chatting, as they might wish. Once seated, many focus group moderators begin with a relevant icebreaker. This could be simple introductions that have some meaning or connection to the focus. In the case of the ASA task force focus groups discussed above, we asked people to introduce themselves and where they were working/studying (“Hi, I’m Allison, and I am a professor at Oregon State University”). You will also want to introduce yourself and the study in simple terms. They’ve already read the consent form, but you would be surprised at how many people ignore the details there or don’t remember them. Briefly talking about the study and then letting people ask any follow-up questions lays a good foundation for a successful discussion, as it reminds everyone what the point of the event is.

Focus groups should convene for between forty-five and ninety minutes. Of course, you must tell the participants the time you have chosen in advance, and you must promptly end at the time allotted. Do not make anyone nervous by extending the time. Let them know at the outset that you will adhere to this timeline. This should reduce the nervous checking of phones and watches and wall clocks as the end time draws near.

Set ground rules and expectations for the group discussion. My preference is to begin with a general question and let whoever wants to answer it do so, but other moderators expect each person to answer most questions. Explain how much cross-talk you will permit (or encourage). Again, my preference is to allow the group to pick up the ball and run with it, so I will sometimes keep my head purposefully down so that they engage with one another rather than me, but I have seen other moderators take a much more engaged position. Just be clear at the outset about what your expectations are. You may or may not want to explain how the group should deal with those who would dominate the conversation. Sometimes, simply stating at the outset that all voices should be heard is enough to create a more egalitarian discourse. Other times, you will have to actively step in to manage (moderate) the exchange to allow more voices to be heard. Finally, let people know they are free to get up to get more coffee or leave the room as they need (if you are OK with this). You may ask people to refrain from using their phones during the duration of the discussion. That is up to you too.

Either before or after the introductions (your call), begin recording the discussion with their collective permission and knowledge . If you have brought a friend or colleague to assist you (as you should), have them attend to the recording. Explain the role of your colleague to the group (e.g., they will monitor the recording and will take short notes throughout to help you when you read the transcript later; they will be a silent observer).

Once the focus group gets going, it may be difficult to keep up. You will need to make a lot of quick decisions during the discussion about whether to intervene or let it go unguided. Only you really care about the research question or topic, so only you will really know when the discussion is truly off topic. However you handle this, keep your “participation” to a minimum. According to Lune and Berg ( 2018:95 ), the moderator’s voice should show up in the transcript no more than 10 percent of the time. By the way, you should also ask your research assistant to take special note of the “intensity” of the conversation, as this may be lost in a transcript. If there are people looking overly excited or tapping their feet with impatience or nodding their heads in unison, you want some record of this for future analysis.

I’m not sure why this stuck with me, but I thought it would be interesting to share. When I was reviewing my plan for conducting focus groups with one of my committee members, he suggested that I give the participants their gift cards first. The incentive for participating in the study was a gift card of their choice, and typical processes dictate that participants must complete the study in order to receive their gift card. However, my committee member (who is Native himself) suggested I give it at the beginning. As a qualitative researcher, you build trust with the people you engage with. You are asking them to share their stories with you, their intimate moments, their vulnerabilities, their time. Not to mention that Native people are familiar with being academia’s subjects of interest with little to no benefit to be returned to them. To show my appreciation, one of the things I could do was to give their gifts at the beginning, regardless of whether or not they completed participating.

—Susanna Y. Park, PhD, mixed-methods researcher in public health and author of “How Native Women Seek Support as Survivors of Intimate Partner Violence: A Mixed-Methods Study”

After the Focus Group

Your “data” will be either fieldnotes taken during the focus group or, more desirably, transcripts of the recorded exchange. If you do not have permission to record the focus group discussion, make sure you take very clear notes during the exchange and then spend a few hours afterward filling them in as much as possible, creating a rich memo to yourself about what you saw and heard and experienced, including any notes about body language and interactions. Ideally, however, you will have recorded the discussion. It is still a good idea to spend some time immediately after the conclusion of the discussion to write a memo to yourself with all the things that may not make it into the written record (e.g., body language and interactions). This is also a good time to journal about or create a memo with your initial researcher reactions to what you saw, noting anything of particular interest that you want to come back to later on (e.g., “It was interesting that no one thought Joe should quit his job, but in the other focus group, half of the group did. I wonder if this has something to do with the fact that all the participants were first-generation college students. I should pay attention to class background here.”).

Please thank each of your participants in a follow-up email or text. Let them know you appreciated their time and invite follow-up questions or comments.

One of the difficult things about focus group transcripts is keeping speakers distinct. Eventually, you are going to be using pseudonyms for any publication, but for now, you probably want to know who said what. You can assign speaker numbers (“Speaker 1,” “Speaker 2”) and connect those identifications with particular demographic information in a separate document. Remember to clearly separate actual identifications (as with consent forms) to prevent breaches of anonymity. If you cannot identify a speaker when transcribing, you can write, “Unidentified Speaker.” Once you have your transcript(s) and memos and fieldnotes, you can begin analyzing the data (chapters 18 and 19).

Advanced: Focus Groups on Sensitive Topics

Throughout this chapter, I have recommended against raising sensitive topics in focus group discussions. As an introvert myself, I find the idea of discussing personal topics in a group disturbing, and I tend to avoid conducting these kinds of focus groups. And yet I have actually participated in focus groups that do discuss personal information and consequently have been of great value to me as a participant (and researcher) because of this. There are even some researchers who believe this is the best use of focus groups ( de Oliveira 2011 ). For example, Jordan et al. ( 2007 ) argue that focus groups should be considered most useful for illuminating locally sanctioned ways of talking about sensitive issues. So although I do not recommend the beginning qualitative researcher dive into deep waters before they can swim, this section will provide some guidelines for conducting focus groups on sensitive topics. To my mind, these are a minimum set of guidelines to follow when dealing with sensitive topics.

First, be transparent about the place of sensitive topics in your focus group. If the whole point of your focus group is to discuss something sensitive, such as how women gain support after traumatic sexual assault events, make this abundantly clear in your consent form and recruiting materials. It is never appropriate to blindside participants with sensitive or threatening topics .

Second, create a confidentiality form (figure 12.2) for each participant to sign. These forms carry no legal weight, but they do create an expectation of confidentiality for group members.

In order to respect the privacy of all participants in [insert name of study here], all parties are asked to read and sign the statement below. If you have any reason not to sign, please discuss this with [insert your name], the researcher of this study, I, ________________________, agree to maintain the confidentiality of the information discussed by all participants and researchers during the focus group discussion.

Signature: _____________________________ Date: _____________________

Researcher’s Signature:___________________ Date:______________________

Figure 12.2 Confidentiality Agreement of Focus Group Participants

Third, provide abundant space for opting out of the discussion. Participants are, of course, always permitted to refrain from answering a question or to ask for the recording to be stopped. It is important that focus group members know they have these rights during the group discussion as well. And if you see a person who is looking uncomfortable or like they want to hide, you need to step in affirmatively and remind everyone of these rights.

Finally, if things go “off the rails,” permit yourself the ability to end the focus group. Debrief with each member as necessary.

Further Readings

Barbour, Rosaline. 2018. Doing Focus Groups . 2nd ed. Thousand Oaks, CA: SAGE. Written by a medical sociologist based in the UK, this is a good how-to guide for conducting focus groups.

Gibson, Faith. 2007. “Conducting Focus Groups with Children and Young People: Strategies for Success.” Journal of Research in Nursing 12(5):473–483. As the title suggests, this article discusses both methodological and practical concerns when conducting focus groups with children and young people and offers some tips and strategies for doing so effectively.

Hopkins, Peter E. 2007. “Thinking Critically and Creatively about Focus Groups.” Area 39(4):528–535. Written from the perspective of critical/human geography, Hopkins draws on examples from his own work conducting focus groups with Muslim men. Useful for thinking about positionality.

Jordan, Joanne, Una Lynch, Marianne Moutray, Marie-Therese O’Hagan, Jean Orr, Sandra Peake, and John Power. 2007. “Using Focus Groups to Research Sensitive Issues: Insights from Group Interviews on Nursing in the Northern Ireland ‘Troubles.’” International Journal of Qualitative Methods 6(4), 1–19. A great example of using focus groups productively around emotional or sensitive topics. The authors suggest that focus groups should be considered most useful for illuminating locally sanctioned ways of talking about sensitive issues.

Merton, Robert K., Marjorie Fiske, and Patricia L. Kendall. 1956. The Focused Interview: A Manual of Problems and Procedures . New York: Free Press. This is one of the first classic texts on conducting interviews, including an entire chapter devoted to the “group interview” (chapter 6).

Morgan, David L. 1986. “Focus Groups.” Annual Review of Sociology 22:129–152. An excellent sociological review of the use of focus groups, comparing and contrasting to both surveys and interviews, with some suggestions for improving their use and developing greater rigor when utilizing them.

de Oliveira, Dorca Lucia. 2011. “The Use of Focus Groups to Investigate Sensitive Topics: An Example Taken from Research on Adolescent Girls’ Perceptions about Sexual Risks.” Cien Saude Colet 16(7):3093–3102. Another example of discussing sensitive topics in focus groups. Here, the author explores using focus groups with teenage girls to discuss AIDS, risk, and sexuality as a matter of public health interest.

Peek, Lori, and Alice Fothergill. 2009. “Using Focus Groups: Lessons from Studying Daycare Centers, 9/11, and Hurricane Katrina.” Qualitative Research 9(1):31–59. An examination of the efficacy and value of focus groups by comparing three separate projects: a study of teachers, parents, and children at two urban daycare centers; a study of the responses of second-generation Muslim Americans to the events of September 11; and a collaborative project on the experiences of children and youth following Hurricane Katrina. Throughout, the authors stress the strength of focus groups with marginalized, stigmatized, or vulnerable individuals.

Wilson, Valerie. 1997. “Focus Groups: A Useful Qualitative Method for Educational Research?” British Educational Research Journal 23(2):209–224. A basic description of how focus groups work using an example from a study intended to inform initiatives in health education and promotion in Scotland.

• Note that I have included a few examples of conducting focus groups with sensitive issues in the “ Further Readings ” section and have included an “ Advanced: Focus Groups on Sensitive Topics ” section on this area. ↵

A focus group interview is an interview with a small group of people on a specific topic.  “The power of focus groups resides in their being focused” (Patton 2002:388).  These are sometimes framed as “discussions” rather than interviews, with a discussion “moderator.”  Alternatively, the focus group is “a form of data collection whereby the researcher convenes a small group of people having similar attributes, experiences, or ‘focus’ and leads the group in a nondirective manner.  The objective is to surface the perspectives of the people in the group with as minimal influence by the researcher as possible” (Yin 2016:336).  See also diversity focus group and convergence focus group.

A form of focus group construction in which people with diverse perspectives and experiences are chosen for inclusion.  This helps the researcher identify commonalities across this diversity and/or note interactions across differences.  Contrast with a convergence focus group

A form of focus group construction in which people with similar perspectives and experiences are included.  These are particularly helpful for identifying shared patterns and group consensus.  Contrast with a diversity focus group .

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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Focus Groups – Steps, Examples and Guide

Table of Contents

Focus Group

Definition:

A focus group is a qualitative research method used to gather in-depth insights and opinions from a group of individuals about a particular product, service, concept, or idea.

The focus group typically consists of 6-10 participants who are selected based on shared characteristics such as demographics, interests, or experiences. The discussion is moderated by a trained facilitator who asks open-ended questions to encourage participants to share their thoughts, feelings, and attitudes towards the topic.

Focus groups are an effective way to gather detailed information about consumer behavior, attitudes, and perceptions, and can provide valuable insights to inform decision-making in a range of fields including marketing, product development, and public policy.

Types of Focus Group

The following are some types or methods of Focus Groups:

Traditional Focus Group

This is the most common type of focus group, where a small group of people is brought together to discuss a particular topic. The discussion is typically led by a skilled facilitator who asks open-ended questions to encourage participants to share their thoughts and opinions.

Mini Focus Group

A mini-focus group involves a smaller group of participants, typically 3 to 5 people. This type of focus group is useful when the topic being discussed is particularly sensitive or when the participants are difficult to recruit.

Dual Moderator Focus Group

In a dual-moderator focus group, two facilitators are used to manage the discussion. This can help to ensure that the discussion stays on track and that all participants have an opportunity to share their opinions.

Teleconference or Online Focus Group

Teleconferences or online focus groups are conducted using video conferencing technology or online discussion forums. This allows participants to join the discussion from anywhere in the world, making it easier to recruit participants and reducing the cost of conducting the focus group.

Client-led Focus Group

In a client-led focus group, the client who is commissioning the research takes an active role in the discussion. This type of focus group is useful when the client has specific questions they want to ask or when they want to gain a deeper understanding of their customers.

The following Table can explain Focus Group types more clearly

How To Conduct a Focus Group

To conduct a focus group, follow these general steps:

Define the Research Question

Identify the key research question or objective that you want to explore through the focus group. Develop a discussion guide that outlines the topics and questions you want to cover during the session.

Recruit Participants

Identify the target audience for the focus group and recruit participants who meet the eligibility criteria. You can use various recruitment methods such as social media, online panels, or referrals from existing customers.

Select a Venue

Choose a location that is convenient for the participants and has the necessary facilities such as audio-visual equipment, seating, and refreshments.

Conduct the Session

During the focus group session, introduce the topic, and review the objectives of the research. Encourage participants to share their thoughts and opinions by asking open-ended questions and probing deeper into their responses. Ensure that the discussion remains on topic and that all participants have an opportunity to contribute.

Record the Session

Use audio or video recording equipment to capture the discussion. Note-taking is also essential to ensure that you capture all key points and insights.

Analyze the data

Once the focus group is complete, transcribe and analyze the data. Look for common themes, patterns, and insights that emerge from the discussion. Use this information to generate insights and recommendations that can be applied to the research question.

When to use Focus Group Method

The focus group method is typically used in the following situations:

Exploratory Research

When a researcher wants to explore a new or complex topic in-depth, focus groups can be used to generate ideas, opinions, and insights.

Product Development

Focus groups are often used to gather feedback from consumers about new products or product features to help identify potential areas for improvement.

Marketing Research

Focus groups can be used to test marketing concepts, messaging, or advertising campaigns to determine their effectiveness and appeal to different target audiences.

Customer Feedback

Focus groups can be used to gather feedback from customers about their experiences with a particular product or service, helping companies improve customer satisfaction and loyalty.

Public Policy Research

Focus groups can be used to gather public opinions and attitudes on social or political issues, helping policymakers make more informed decisions.

Examples of Focus Group

Here are some real-time examples of focus groups:

• A tech company wants to improve the user experience of their mobile app. They conduct a focus group with a diverse group of users to gather feedback on the app’s design, functionality, and features. The focus group consists of 8 participants who are selected based on their age, gender, ethnicity, and level of experience with the app. During the session, a trained facilitator asks open-ended questions to encourage participants to share their thoughts and opinions on the app. The facilitator also observes the participants’ behavior and reactions to the app’s features. After the focus group, the data is analyzed to identify common themes and issues raised by the participants. The insights gathered from the focus group are used to inform improvements to the app’s design and functionality, with the goal of creating a more user-friendly and engaging experience for all users.
• A car manufacturer wants to develop a new electric vehicle that appeals to a younger demographic. They conduct a focus group with millennials to gather their opinions on the design, features, and pricing of the vehicle.
• A political campaign team wants to develop effective messaging for their candidate’s campaign. They conduct a focus group with voters to gather their opinions on key issues and identify the most persuasive arguments and messages.
• A restaurant chain wants to develop a new menu that appeals to health-conscious customers. They conduct a focus group with fitness enthusiasts to gather their opinions on the types of food and drinks that they would like to see on the menu.
• A healthcare organization wants to develop a new wellness program for their employees. They conduct a focus group with employees to gather their opinions on the types of programs, incentives, and support that would be most effective in promoting healthy behaviors.
• A clothing retailer wants to develop a new line of sustainable and eco-friendly clothing. They conduct a focus group with environmentally conscious consumers to gather their opinions on the design, materials, and pricing of the clothing.

Purpose of Focus Group

The key objectives of a focus group include:

Generating New Ideas and insights

Focus groups are used to explore new or complex topics in-depth, generating new ideas and insights that may not have been previously considered.

Understanding Consumer Behavior

Focus groups can be used to gather information on consumer behavior, attitudes, and perceptions to inform marketing and product development strategies.

Testing Concepts and Ideas

Focus groups can be used to test marketing concepts, messaging, or product prototypes to determine their effectiveness and appeal to different target audiences.

Gathering Customer Feedback

Informing decision-making.

Focus groups can provide valuable insights to inform decision-making in a range of fields including marketing, product development, and public policy.

Advantages of Focus Group

The advantages of using focus groups are:

• In-depth insights: Focus groups provide in-depth insights into the attitudes, opinions, and behaviors of a target audience on a specific topic, allowing researchers to gain a deeper understanding of the issues being explored.
• Group dynamics: The group dynamics of focus groups can provide additional insights, as participants may build on each other’s ideas, share experiences, and debate different perspectives.
• Efficient data collection: Focus groups are an efficient way to collect data from multiple individuals at the same time, making them a cost-effective method of research.
• Flexibility : Focus groups can be adapted to suit a range of research objectives, from exploratory research to concept testing and customer feedback.
• Real-time feedback: Focus groups provide real-time feedback on new products or concepts, allowing researchers to make immediate adjustments and improvements based on participant feedback.
• Participant engagement: Focus groups can be a more engaging and interactive research method than surveys or other quantitative methods, as participants have the opportunity to express their opinions and interact with other participants.

Limitations of Focus Groups

While focus groups can provide valuable insights, there are also some limitations to using them.

• Small sample size: Focus groups typically involve a small number of participants, which may not be representative of the broader population being studied.
• Group dynamics : While group dynamics can be an advantage of focus groups, they can also be a limitation, as dominant personalities may sway the discussion or participants may not feel comfortable expressing their true opinions.
• Limited generalizability : Because focus groups involve a small sample size, the results may not be generalizable to the broader population.
• Limited depth of responses: Because focus groups are time-limited, participants may not have the opportunity to fully explore or elaborate on their opinions or experiences.
• Potential for bias: The facilitator of a focus group may inadvertently influence the discussion or the selection of participants may not be representative, leading to potential bias in the results.
• Difficulty in analysis : The qualitative data collected in focus groups can be difficult to analyze, as it is often subjective and requires a skilled researcher to interpret and identify themes.

Characteristics of Focus Group

• Small group size: Focus groups typically involve a small number of participants, ranging from 6 to 12 people. This allows for a more in-depth and focused discussion.
• Targeted participants: Participants in focus groups are selected based on specific criteria, such as age, gender, or experience with a particular product or service.
• Facilitated discussion: A skilled facilitator leads the discussion, asking open-ended questions and encouraging participants to share their thoughts and experiences.
• I nteractive and conversational: Focus groups are interactive and conversational, with participants building on each other’s ideas and responding to one another’s opinions.
• Qualitative data: The data collected in focus groups is qualitative, providing detailed insights into participants’ attitudes, opinions, and behaviors.
• Non-threatening environment: Participants are encouraged to share their thoughts and experiences in a non-threatening and supportive environment.
• Limited time frame: Focus groups are typically time-limited, lasting between 1 and 2 hours, to ensure that the discussion stays focused and productive.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Your Brain Can Only Take So Much Focus

• Srini Pillay

“Unfocus” enhances resilience, creativity, and decision making.

Research has shed light on the power of focus and its role as a hidden driver of success. Yet as helpful as focus can be, research also shows there’s a downside to it: excessive focus exhausts the focus circuits in your brain. It can drain your energy, make you lose self-control, impair your decision-making, and make you less collaborative. The brain operates optimally when it toggles between focus and unfocus. When you unfocus, you engage a brain circuit called the default mode network (DMN). The DMN activates old memories, goes back and forth between the past, present, and future, and recombines different ideas. Using this new and previously inaccessible data, you can imagine creative solutions or predict the future, and more. There are many simple and effective ways to activate this circuit in the course of a day, such as positive constructive daydreaming, napping, and consciously thinking from another person’s perspective.

The ability to focus is an important driver of excellence. Focused techniques such as to-do lists , timetables, and calendar reminders all help people to stay on task. Few would argue with that, and even if they did, there is evidence to support the idea that resisting distraction and staying present have benefits: practicing mindfulness for 10 minutes a day , for example, can enhance leadership effectiveness by helping you become more able to regulate your emotions and make sense of past experiences . Yet as helpful as focus can be, there’s also a downside to focus as it is commonly viewed.

• Srini Pillay , M.D. is an executive coach and CEO of NeuroBusiness Group . He is also a technology innovator and entrepreneur in the health and leadership development sectors, and an award-winning author. His latest book is Tinker, Dabble, Doodle, Try: Unlock the Power of the Unfocused Mind . He is also a part-time Assistant Professor at Harvard Medical School and teaches in the Executive Education Programs at Harvard Business School and Duke Corporate Education, and is on internationally recognized think tanks.

Partner Center

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

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods

What is a focus group in research?

Why are focus groups effective in research, what are some research examples of focus groups, planning and conducting focus groups, challenges and limitations of focus groups.

• Observational research
• Case studies
• Ethnographical research
• Ethical considerations

Confidentiality and privacy

• Power dynamics
• Reflexivity

Focus groups

Focus groups are a widely used qualitative research method in which a small group of participants engage in guided discussions on a specific topic. You might think of a focus group as a group interview because it can gather information on people's experiences, opinions, and feelings in a natural and interactive setting. However, the group dynamic of a focus group discussion can also be especially useful for observing how people construct meaning together, practice body language, and interact with each other.

In this section, we'll discuss the focus group method, compare it to interview research, and explore what researchers can do with focus group data.

Focus groups are characterized by their collaborative, interactive nature, with discussions guided by a facilitator or moderator. These qualities raise some similarities with and differences from qualities found in interview research .

What is the purpose of a focus group?

Like interviews, focus groups are often used to elicit opinions and perspectives about a topic, product, or service. Market research often employs focus group discussions to test out something new before it is introduced to the larger public. However, a focus group can also illuminate social behavior by allowing researchers to observe how people interact with each other in a way that wouldn't be possible with interviews or observations .

How many people form a focus group?

One key characteristic is the number of focus group participants involved. In this type of research, a moderator will typically work with a small group of 6 to 10 focus group members. This range is considered optimal because it is small enough to allow everyone a chance to share their thoughts and large enough to ensure a diversity of perspectives. Too few participants can limit the richness of the discussion, while too many can make the discussion difficult to manage and may prevent some participants from expressing their views.

What does a focus group do?

In general, a focus group consists of posing questions to a group of people and inviting then to discuss the question or topic. Focus group discussions are typically guided by a set of open-ended questions prepared in advance by the researcher. Ideally, focus group questions serve as prompts to stimulate discussion and to ensure that all relevant topics are covered.

The nature of these questions varies depending on the research objectives. Still, they are generally broad and non-directive, allowing participants the freedom to express their views and experiences in their own words. The role of the moderator is to use these questions to guide the discussion, to probe deeper when necessary, and to ensure that all participants have the opportunity to contribute.

The interaction among group members is the defining characteristic that sets focus groups apart from other qualitative research methods like individual interviews. They allow researchers to observe how opinions are formed and influenced within a social context. Through these interactions, researchers can gain insights into not only individual attitudes and beliefs but also the group dynamics that shape these attitudes and beliefs.

The interaction among participants can stimulate new thoughts and ideas, reveal points of agreement or disagreement, and highlight the process of consensus-building or negotiation that occurs in a group setting. The moderator plays a crucial role in facilitating these interactions, encouraging participation, managing conflicts, and maintaining a constructive and respectful discussion environment.

Focus groups are used in a variety of research settings, from market research to social science studies, due to their versatility in collecting qualitative data . They provide a rich source of information as they capture not only what people think but also how they think and why they think the way they do. Let's look at some of the potential applications of focus groups in research.

Exploratory research

Focus groups are particularly valuable in exploratory research, which is often the first step in investigating a new or complex issue. Exploratory research aims to gain a general understanding of a problem, and focus groups are well-suited for this task due to their interactive and dynamic nature. They can help researchers identify key themes , generate propositions, and develop a deeper understanding of the research context. By encouraging open-ended discussion, these group interactions can reveal a breadth of perspectives and experiences and uncover issues and insights that researchers may not have anticipated.

Idea generation

The group dynamics of focus groups can stimulate creative thinking and the generation of new ideas. This can be particularly beneficial in fields such as product development, policy making, and program design. In these settings, focus groups can help researchers or practitioners gather a range of ideas about a new product, policy, or program, which can then be further refined and evaluated.

Language and terminology

Focus groups can also provide valuable insights into the language and terms that participants use to discuss a certain topic. This is particularly important in qualitative research, where the goal is often to understand the meanings and interpretations that people attach to their experiences. The language used in focus group discussions can reveal these meanings and interpretations and help researchers develop a more nuanced understanding of the topic under study. This understanding can also be particularly useful when developing survey instruments or interpreting other qualitative data.

Assessing concepts and prototypes

Focus groups can also be used to assess concepts and prototypes. For example, in marketing research, a focus group might be used to gather feedback on a new product design or to understand how potential users interact with a prototype. In social science research, focus groups might be used to refine and verify concepts or theories that are relevant to group behavior. This kind of feedback can help researchers and practitioners hone their ideas based on the social interactions of the focus group.

Observing social interaction online

The advent of digital technologies has expanded the possibilities for observing social interaction through the use of online focus groups. Online focus groups, conducted via video conferencing platforms, chat rooms, or discussion forums, offer similar benefits to their in-person counterparts but with added flexibility. They allow participants from diverse geographical locations to engage in discussion, and they can be more convenient and less intimidating for some participants.

Moreover, online focus groups can provide a written or recorded transcript of the discussion, which can be useful for analysis. However, they also present unique challenges, such as managing group dynamics in a virtual environment and ensuring access and comfort with the necessary technology among participants.

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Many different fields use focus groups both as a means to collect honest opinions about key research topics or to observe human behavior and interaction. Let's look at some of the many fields that employ a focus group format in research.

Consumer behavior: In market research, focus groups are often used to understand consumer preferences and attitudes toward products or services. For instance, a company might use a focus group to gauge consumer reactions to a new product concept or to understand the reasons behind purchasing decisions.

Healthcare: In healthcare research, focus groups have been used to explore patients' experiences and perceptions of healthcare services or to understand the attitudes and beliefs of healthcare providers. For example, a study might conduct focus groups with patients to gain insights into the barriers and facilitators to medication adherence.

Education: In educational research, focus groups can be used to understand student experiences, attitudes, and beliefs. For instance, a university might use focus groups to explore students' perceptions of campus safety, or a school district might conduct focus group discussions with teachers to understand the challenges they face in implementing a new curriculum.

Public policy: Focus groups can provide valuable insights into the formulation and evaluation of public policies. They can be used to understand public perceptions and attitudes toward policy proposals or to gather feedback on existing policies. For example, a local government might use focus groups to explore residents' views on a proposed transportation policy.

As with interviews, conducting a focus group isn't simply a matter of having people in the same place and talking to them. Focus group research methods call for intentional planning and organization. Here are some considerations to keep in mind when thinking about focus groups.

Selecting participants

The selection of participants is a crucial step in planning a focus group. Participants should be chosen based on their relevance to the research question. They might share a common characteristic (e.g., they are users of a particular service, or they belong to a specific age group), or they may represent a diversity of perspectives on the topic under discussion.

The group should be small enough to manage (typically 6-10 participants) but large enough to ensure a variety of views. In some cases, researchers might choose to conduct multiple focus groups to compare and contrast different groups’ views.

Developing a discussion guide

A discussion guide outlines the topics to be discussed during the focus group. It typically includes a list of open-ended questions and prompts that are designed to stimulate discussion on the research topic.

The questions should be thoughtfully constructed and sequenced, starting with broader questions to warm up the group and progressively focusing on more specific areas of interest. While the discussion guide serves as a roadmap for the session, the moderator should be flexible and responsive to the flow of the discussion, probing for deeper insights and following up on interesting or unexpected comments.

Role of the moderator

The role of the moderator is central to the success of a focus group. A skilled moderator facilitates the discussion, encourages participation, manages group dynamics, and ensures that all topics in the discussion guide are covered. The moderator needs to ensure each participant gets a chance to express their views, and it is also helpful to keep participants from speaking over one another so that everyone can be heard, both during the discussion and for subsequent transcription.

The moderator needs to create an environment where participants feel comfortable sharing their views while also ensuring that the discussion remains focused and productive. This requires a balance of active listening, gentle steering, and tactful intervention when necessary.

Managing group dynamics

Managing group dynamics is a key challenge in focus groups. The interaction among participants can stimulate rich and insightful discussions, but it can also lead to issues such as dominance by a few participants, groupthink, or conflicts. As a result, the moderator plays a crucial role in managing these dynamics, encouraging quieter participants to speak, respectfully managing more dominant participants, and facilitating a constructive and respectful discussion environment.

However, the extent to which the moderator controls the discussion may depend on the research inquiry driving the focus group, particularly if the study is concerned with observing a particular behavior or group dynamic. A fruitful focus group discussion often consists of participants speaking with each other, as opposed to each participant simply answering the moderator one by one.

Focus group question examples

Designing focus group questions is an art in itself, with a focus on sparking discussion and interaction among participants. Here are some example questions that are particularly suited for focus groups:

• "How do others here feel about what [participant's name] just said?" This question can encourage participants to respond to each other's views, fostering a more interactive discussion.
• "Can anyone provide a different perspective on this issue?" This prompt invites diversity of opinion and encourages quieter participants to contribute.
• "Why do you think people might have different opinions about this topic?" This question can stimulate discussion about the reasons behind varying perspectives.
• "Can you help me understand why this is important to you?" By asking for elaboration, this question can lead to deeper, more nuanced discussions.
• "Has anyone had a different experience?" This question can bring out a range of experiences and perspectives within the group.
• "How do you think others outside of this group might view this issue?" This question encourages participants to consider perspectives beyond their own, fostering empathy and understanding.

By crafting questions that prompt group interaction and discussion, researchers can harness the full potential of the focus group method.

At first glance, a focus group is a great way to quickly capture the perspectives of multiple participants. That said, meeting this goal has its challenges. Let's discuss some of them briefly.

Recruitment and participation

One of the key challenges in conducting focus groups is recruiting and retaining an appropriate group of participants. Given the group-based nature of this method, a single participant dropping out can significantly impact the dynamics and the effectiveness of the session. Ensuring a diversity of views while also creating a comfortable environment for open discussion can be a delicate balance to strike. Additionally, scheduling a time that is convenient for all participants can be logistically challenging, particularly when dealing with busy or hard-to-reach populations.

Interpreting group dynamics

While the interaction in focus groups can generate rich insights, it can also complicate the interpretation of the data. The dynamics of the group discussion can influence individual responses, with dominant personalities potentially skewing the discussion or quieter participants holding back their views. It can be challenging for researchers to discern whether the views expressed represent the individual's true beliefs, the influence of the group dynamic, or a combination of both.

Depth of individual perspectives

Unlike other research methods , focus groups can provide a broad overview of group opinions and norms. However, they may not allow for the depth of understanding of individual experiences and perspectives that can be achieved through other qualitative methods, like one-on-one interviews. Time constraints and the need to ensure all participants have a chance to speak can limit the depth of exploration into individual views and experiences.

Transcription and data analysis

Other methods, such as surveys and interviews , generate data that is relatively easier to organize. Survey data is often divided into records, each representing a particular individual, while each and every interview has its own separate raw audio and corresponding transcript. A focus group has multiple participants who may contribute spontaneously to a discussion and even talk over each other. Transcribing these interactions for the purposes of coding and data analysis can be time-consuming as the researcher needs to discern between different voices and adequately represent these voices for empirical analysis.

Ethical considerations for focus groups

As with all other qualitative research methods, ethical issues such as informed consent and vulnerable populations are relevant to focus group discussions. However, there are also ethical considerations that are unique to focus groups that are worth thinking about.

Potential for unintended disclosure

In a focus group, there's a distinct possibility that participants may disclose more personal or sensitive information than they intended due to the dynamics of the group conversation. This presents an ethical challenge for researchers, as they have a responsibility to protect participants from potential harm, including emotional distress that might result from such disclosures. Researchers should be prepared to manage these situations by providing immediate support if necessary, reminding participants about the voluntary nature of their participation and their right to pass on any question, and following up with participants after the session if appropriate.

Protecting the confidentiality and privacy of participants is another key ethical consideration. In focus groups, this can be more challenging than in one-on-one interviews because there are multiple participants. Researchers should ensure that participants understand the importance of confidentiality, which includes not disclosing any information revealed during the discussion with people outside the focus group. The researcher should also take steps to protect participants' privacy in the research report, such as by using pseudonyms or other de-identifying methods. Online focus groups present additional privacy considerations, such as data security and the potential for participants to be identified through their online profiles.

Managing sensitive topics

Focus group discussions can sometimes involve sensitive topics that may cause discomfort or distress for participants. Researchers need to be prepared to manage these situations with ethical awareness and sensitivity. This includes being aware of potential triggers, providing support or referrals to support services if necessary, and ensuring that the discussion remains respectful and safe for all participants.

Respect for diversity

Given the group nature of focus groups, respect for diversity is an important ethical consideration. This includes being sensitive to and respectful of differences in culture, age, gender, socioeconomic status, and other factors among participants. Researchers should foster an inclusive and respectful discussion environment and should be mindful of potential power dynamics or biases that could influence the discussion.

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What is Qualitative in Qualitative Research

Patrik aspers.

1 Department of Sociology, Uppsala University, Uppsala, Sweden

2 Seminar for Sociology, Universität St. Gallen, St. Gallen, Switzerland

3 Department of Media and Social Sciences, University of Stavanger, Stavanger, Norway

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

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

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

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

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

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

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

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

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

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

Towards a Definition of Qualitative Research

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

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

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

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

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

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

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

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

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

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

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

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

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

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

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

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

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

Analysis – What is Qualitative Research?

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

Qualitative and Quantitative

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

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

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

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

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

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

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

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

Qualitative Research

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

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

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

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

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

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

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

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

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

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

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

Grounded Theory

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

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

Defining Qualitative Research

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

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

Distinctions

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

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

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

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

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

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

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

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

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

Improved Understanding

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

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

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

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

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

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

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

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

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

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

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

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

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

Acknowledgements

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

Biographies

is professor of sociology at the Department of Sociology, Uppsala University and Universität St. Gallen. His main focus is economic sociology, and in particular, markets. He has published numerous articles and books, including Orderly Fashion (Princeton University Press 2010), Markets (Polity Press 2011) and Re-Imagining Economic Sociology (edited with N. Dodd, Oxford University Press 2015). His book Ethnographic Methods (in Swedish) has already gone through several editions.

is associate professor of sociology at the Department of Media and Social Sciences, University of Stavanger. His research has been published in journals such as Social Psychology Quarterly, Sociological Theory, Teaching Sociology, and Music and Arts in Action. As an ethnographer he is working on a book on he social world of big-wave surfing.

Publisher’s Note

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Contributor Information

Patrik Aspers, Email: [email protected] .

Ugo Corte, Email: [email protected] .

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This paper is in the following e-collection/theme issue:

Published on 3.6.2024 in Vol 26 (2024)

Electronic Health Literacy Scale-Web3.0 for Older Adults with Noncommunicable Diseases: Validation Study

Authors of this article:

Original Paper

• Wenfei Cai 1 , MEd   ; 
• Wei Liang 1 , PhD   ; 
• Huaxuan Liu 2 , PhD   ; 
• Rundong Zhou 1 , MEd   ; 
• Jie Zhang 1 , PhD   ; 
• Lin Zhou 3 , PhD   ; 
• Ning Su 1 , PhD   ; 
• Hanxiao Zhu 1 , PhD   ; 
• Yide Yang 4 , PhD  

1 School of Physical Education, Shenzhen University, Shenzhen, China

2 School of Physical Education and Sport Science, Fujian Normal University, Fuzhou, China

3 School of Physical Education, Hebei Normal University, Shijiazhuang, China

4 School of Medicine, Hunan Normal University, Changsha, China

Corresponding Author:

Wei Liang, PhD

School of Physical Education

Shenzhen University

3688 Nanhai Road, Nanshan District

Shenzhen, 518060

Phone: 86 15217940540

Email: [email protected]

Background: In the current digital era, eHealth literacy plays an indispensable role in health care and self-management among older adults with noncommunicable diseases (NCDs). Measuring eHealth literacy appropriately and accurately ensures the successful implementation and evaluation of pertinent research and interventions. However, existing eHealth literacy measures focus mainly on individuals’ abilities of accessing and comprehending eHealth information (Web1.0), whereas the capabilities for web-based interaction (Web2.0) and using eHealth information (Web3.0) have not been adequately evaluated.

Objective: This study aimed to examine the reliability, validity, and measurement invariance of the eHealth Literacy Scale-Web3.0 (eHLS-Web3.0) among older adults with NCDs.

Methods: A total of 642 Chinese older adults with NCDs (mean age 65.78, SD 3.91 years; 55.8% female) were recruited in the baseline assessment, of whom 134 (mean age 65.63, SD 3.99 years; 58.2% female) completed the 1-month follow-up assessment. Baseline measures included the Chinese version of the 24-item 3D eHLS-Web3.0, the Chinese version of the 8-item unidimensional eHealth Literacy Scale (eHEALS), and demographic information. Follow-up measures included the 24-item eHLS-Web3.0 and accelerometer-measured physical activity and sedentary behavior. A series of statistical analyses, for example, Cronbach α, composite reliability coefficient (CR), confirmatory factor analysis (CFA), and multigroup CFA, were performed to examine the internal consistency and test-retest reliabilities, as well as the construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence.

Results: Cronbach α and CR were within acceptable ranges of 0.89-0.94 and 0.90-0.97, respectively, indicating adequate internal consistency of the eHLS-Web3.0 and its subscales. The eHLS-Web3.0 also demonstrated cross-time stability, with baseline and follow-up measures showing a significant intraclass correlation of 0.81-0.91. The construct validity of the 3D structure model of the eHLS-Web3.0 was supported by confirmatory factor analyses. The eHLS-Web3.0 exhibited convergent validity with an average variance extracted value of 0.58 and a CR value of 0.97. Discriminant validity was supported by CFA results for a proposed 4-factor model integrating the 3 eHLS-Web3.0 subscales and eHEALS. The predictive validity of the eHLS-Web3.0 for health behaviors was supported by significant associations of the eHLS-Web3.0 with light physical activity (β=.36, P =.004), moderate to vigorous physical activity ( β =.49, P <.001), and sedentary behavior ( β =–.26, P =.002). Finally, the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence was supported by the establishment of configural, metric, strong, and strict invariances.

Conclusions: The present study provides timely empirical evidence on the reliability, validity, and measurement invariance of the eHLS-Web3.0, suggesting that the 24-item 3D eHLS-Web3.0 is an appropriate and valid tool for measuring eHealth literacy among older adults with NCDs within the Web3.0 sphere.

Introduction

Noncommunicable diseases (NCDs), known as chronic diseases, result in the mortality of 41 million people annually, equivalent to approximately 74% of all global deaths [ 1 ]. Characterized by high morbidity, high mortality, low control rates, and limited awareness, NCDs impose a considerable financial burden on individuals, their families, and society as a whole, particularly among older patients [ 2 ]. In China, the prevalence rate of NCDs among older adults aged 60 years and older was 50%-75%, as reported in recent epidemiological studies [ 3 - 5 ]. Therefore, NCDs in older adults are a vital public health concern, and their management is a global challenge.

Previous evidence has demonstrated that empowering and educating patients with NCDs to focus on self-management and health promotion is essential [ 2 , 6 ]. Enabling patients to inquire about their medical status, comply with medication instructions, enhance their engagement and compliance in the health care process, adopt healthier lifestyles, and ultimately reduce reliance on constant supervision from health care professionals is a challenging task [ 6 , 7 ]. Nevertheless, facilitating patient self-care is a critical step toward improving the overall health status and alleviating the burden on health care facilities, especially within low- and middle-income countries [ 7 , 8 ].

With the rapid advancement of technology, the internet has become the quickest and most easily accessible resource for obtaining and delivering health information, offering ample opportunities for self-management and health promotion [ 6 , 9 ]. Recent review studies have consistently shown that internet-based health interventions for individuals with NCDs can have a substantial impact on enhancing self-management and patient engagement and compliance with their health care [ 10 , 11 ]. Despite the potential of the internet to improve health care services for NCDs, older adults encounter significant challenges in using digital health technologies [ 12 ]. In particular, the information found on the internet originates from numerous providers and sources that are difficult to regulate, thereby leading to potential problems in terms of accuracy and the potential dissemination of prejudiced content that aligns with the interests and objectives of certain parties involved [ 13 ]. Previous research has highlighted the considerable difficulties faced by older adults in accessing reliable and high-quality health information that addresses their specific health needs [ 14 , 15 ]. Furthermore, studies have revealed that a noteworthy proportion of older internet users lack confidence in their capability to execute basic tasks on the internet [ 15 ]. The challenges mentioned above not only impede older adults from harnessing the internet’s full potential for health care purposes but also exacerbate the digital divide and health disparities [ 12 ]. In such a scenario, eHealth literacy is emphasized in numerous studies as a critical skill that older adults with NCDs must acquire in the digital era of disease management and health care [ 2 , 6 , 16 ].

eHealth literacy, first proposed by Norman and Skinner in 2006 [ 17 ], refers to “individual’s abilities to seek, find, understand, and appraise health information from electronic resources and apply that knowledge to solve a health problem or make a health-related decision.” The concept of eHealth literacy is founded on social cognitive theory, consisting of 6 essential skills or literacies: traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy [ 17 , 18 ]. To provide a general assessment of this concept that can assist in clinical decision-making and health promotion planning for individuals or specific samples, Norman and Skinner [ 17 ] developed an 8-item unidimensional eHealth Literacy Scale (eHEALS). The eHEALS is the most well-known and extensively used instrument for assessing eHealth literacy to date [ 19 ]. The reliability and validity of the eHEALS have been extensively examined in diverse cultural contexts, including English [ 17 , 20 ], German [ 21 ], Spanish [ 22 ], Dutch [ 23 ], Italian [ 24 ], Portuguese [ 25 ], Japanese [ 26 ], and Chinese [ 27 ], providing compelling evidence of its efficacy across multiple languages and cultures.

However, as technology continues to advance, there has been an increasing acknowledgment of the necessity to update the content of eHealth literacy to ensure optimal synchronization with the evolving internet landscape [ 23 , 28 , 29 ]. Several studies have raised concerns regarding the unidimensional nature of the eHEALS, as well as its inadequate performance in psychometric evaluations, particularly when using it to measure the usage of novel technologies in seeking and assessing health information [ 23 , 27 - 30 ]. For example, previous research has shown a weak association between eHEALS and eHealth behaviors beyond web-based information-researching skills, indicating the requirement to update the tool [ 23 ]. Furthermore, a recent systematic review indicated that the structure of the eHEALS varied across multiple studies, where a 2-factor or 3-factor structure was also identified in certain studies [ 19 ]. There has also been some questioning of the variability of the items, even though the eHEALS had the same factor construct [ 19 ]. Alongside the influence of cultural contexts, a primary reason for the inconsistencies of the factor structures and corresponding items may be that the eHEALS is outdated for use in evaluating eHealth literacy in the new digital age [ 19 ].

Indeed, the arguments outlined above are reasonable. As per the widely acknowledged generation divisions of internet evolution, the present internet landscape has progressed through 3 distinct phases, starting with Web1.0 (a read-only web) to Web2.0 (ie, a read-write mode that provides a participatory social web with increased collaboration and interaction among consumers, programmers, service providers, and organization) and to current Web3.0 (ie, a read-write-execute mode that provides digital, personalized, and intelligent services; also known as semantic web) [ 23 , 30 , 31 ]. The eHEALS was developed 15 years ago for measuring individuals’ capability related to reading and viewing within a Web1.0 context, and therefore, it is necessary to update it to effectively scale current eHealth usage.

To fill this gap, numerous new measurement tools of eHealth literacy have been developed. One example is the 20-item extended version of the eHEALS (eHEALS-E) created by Petrič et al [ 32 ], which is designed to better encompass the complicated factors contributing to eHealth literacy. However, the eHEALS-E is based on the same definition as the original eHEALS, and therefore, it may also have limitations in measuring only a narrow aspect of eHealth literacy [ 19 ]. Furthermore, second-generation instruments of eHealth literacy have been developed (eg, eHealth Literacy Scale [eHLS], Digital Health Literacy Instrument [DHLI], Transactional eHealth Literacy Instrument, eHealth Literacy Assessment Toolkit, and Chinese version of the electronic eHealth Literacy Scale [C-eHEALS]) to assess a broader spectrum of eHealth literacy concepts, ensuring their relevance in the age of social media and eHealth [ 19 , 28 ]. These measures have provided novel approaches for evaluating eHealth literacy, with some of them specifically designed to measure web communication capabilities. However, their coverage is limited to Web2.0 skills, and evaluation of eHealth literacy content relevant to Web3.0 technologies remains absent [ 33 ]. Recently, Liu et al [ 33 ] developed a 24-item eHealth Literacy Scale-Web3.0 (eHLS-Web3.0) to measure eHealth skills in the context of Web3.0. Compared with previous eHEALS and second-generation eHealth literacy scales, the eHLS-Web3.0 is an improvement consisting of 3 distinct dimensions (ie, acquisition, verification, and application) that evaluates the abilities covering the entire spectrum of Web1.0 (eg, searching, understanding, and identifying), 2.0 (eg, sharing and interactive communication), and 3.0 (eg, recording, self-managing, applying, and adjusting) [ 33 ]. The reliabilities, validities, and measurement invariance of the eHLS-Web3.0 across gender and region have been supported in a previous study with young adults [ 33 ], whereas its psychometric properties remain unexplored in older adults, especially those living with NCDs. Therefore, further research is needed to investigate the applicability and psychometric properties of the eHLS-Web3.0 in older adults with NCDs, which may inform the development of effective interventions to improve eHealth literacy and promote better health care outcomes in this population.

Given the above, the purpose of this study was to examine the reliability, validity, and measure invariance of the eHLS-Web3.0 in a sample of older adults with NCDs. In particular, this study has 3 main objectives. First, the internal consistency and test-retest reliabilities of the eHLS-Web3.0 would be examined. Based on previous studies [ 12 , 17 , 20 , 34 - 36 ], a conventional 1-month time frame for evaluating the test-retest reliability was used in this study. Second, the construct, convergent, concurrent, discriminant, and predictive validities of the 24-item 3D eHLS-Web3.0 would be ascertained. Because the eHEALS has been proven to be a reliable tool for measuring eHealth literacy among older adults with NCDs in previous studies [ 2 , 17 ], this study would use the eHEALS as the criteria scale for the examination of the concurrent validity of the eHLS-Web3.0. Furthermore, considering the content distinction between the eHLS-Web3.0 and the eHEALS, the discriminant validity of the eHLS-Web3.0 would be examined by comparing the eHLS-Web3.0 subscales and the eHEALS. Additionally, previous studies have established a strong association between eHealth literacy and various health behaviors [ 13 , 37 ]. Specifically, eHealth literacy has been shown to positively correlate with health-promoting behaviors (eg, physical activity) and negatively correlate with risk behaviors (eg, sedentary behavior) among young and older adults [ 38 , 39 ]. Therefore, this study would investigate the predictive validity of the eHLS-Web3.0 for 2 specific health behaviors (ie, physical activity and sedentary behavior), given their crucial impact on the physical and mental well-being of older adults with NCDs [ 40 ]. By identifying the predictive validity of the eHLS-Web3.0, this study is expected to make a noteworthy contribution to future research in this field. Finally, considering that gender, education level, and residence are potential correlates of eHealth literacy [ 6 , 33 , 41 ], the measurement invariance of the eHEALS would be examined at the configural, metric, strong, and strict levels across gender, education level, and residence.

Design, Participants, and Procedure

This study applied a 2-wave prospective design. Considering an item-to-response ratio of 1:10 and the recommendation for a minimum sample size of 200 in confirmatory factor analysis (CFA) [ 42 ], 240 participants were required to ensure a robust statistical estimation. With an approximate response rate of 85% and a prior estimated prevalence rate of NCDs of 50% in older adults [ 4 ], a minimum of 564 participants were required to be contacted at the initial recruitment stage. Eligible participants for the study were older adults who met the following inclusion criteria: (1) aged 60 years or older, (2) experiencing at least 1 type of NCD (eg, cardiovascular diseases, cancer, type 2 diabetes, and obesity), (3) no physical mobility restrictions, (4) no cognitive disorders, (5) proficient in reading and understanding Chinese, and (6) having access to a smartphone or laptop.

Participants were recruited from the outpatient departments of 6 hospitals from 3 cities (Shiyan, Wuhan, and Suizhou) of Hubei Province (Central China) using a convenience sampling approach. The survey was implemented using the SOJUMP web-based survey platform (Changsha Ranxing Information Technology Co, Ltd). Two health care professionals undertook an initial review of the scale items to ensure that the wording was appropriate for older adults with NCDs. Subsequently, 6 older adults with NCDs (3 female and 3 male) were invited to complete a pilot assessment aimed at (1) optimizing the design of the electronic questionnaires (eg, using the large font and highlighting the key information) and (2) refining the language and eliminating any errors to ensure that the scale items were easily comprehensible for the target population.

In the main study, participants were provided with a QR code through nurses to gain access to the web-based survey. Before answering the questionnaires, participants were required to sign an informed consent form on the first page of the survey. The web-based survey lasted approximately 20 minutes. To ensure a robust evaluation for the scale test-retest reliability and predictive validity, a minimum of 100 participants were needed [ 43 ]. Accounting for a potential 30% attrition rate (eg, no response and invalid or missing data) [ 4 ], a total of 142 participants were required for the second-wave data collection. Invitations were sent out randomly via SMS text messages to those who had completed the first round of data collection until enough participants agreed to participate in the follow-up survey, scheduled for 1 month later. Participants who agreed to participate in the second round of investigation were requested to revisit the hospital, where 2 qualified assistants guided them to complete the follow-up web-based survey and provided detailed instructions on the use of the accelerometer for data collection.

Ethical Considerations

This study adhered to the principles outlined in the Declaration of Helsinki by the World Medical Association. The Medical Ethics Committee of the Faculty of Medicine at Shenzhen University reviewed and approved this study (PN-202300066). All participants provided signed informed consent for both the primary study and the sensitivity analyses. The data were anonymized to protect participant privacy, and participation in the study was entirely voluntary. As a token of appreciation, participants received a participation fee of 5 RMB (US $0.7) on completing the data collection.

eHealth Literacy Scale-Web3.0

The 24-item eHLS-Web3.0 was originally developed by Liu et al [ 33 ] for the Chinese adult population. This scale comprises 3 dimensions: acquisition (items 1-4 and 11-14), verification (items 5-10), and application (items 15-24). Responses were indicated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The total score of the scale ranges from 24 to 120, with a higher score indicating a greater level of eHealth literacy. The reliability and validity of the eHLS-Web3.0 have been fully supported by previous research with Chinese young adults (Cronbach α=0.91-0.97).

The 8-item eHEALS was developed by Norman and Skinner [ 17 ] for use among Canadian adolescents. The original scale is unidimensional and has been validated in various countries across diverse populations. The Chinese version of the 8-item eHEALS has been examined in previous studies among older adults with NCDs, where the reliability and validity of the scale have been fully supported (Cronbach α=0.95-0.98).

Health Behaviors

Physical activity and sedentary behavior were measured using the ActiGraph GT3X+ (ActiGraph) on the right side of the waist for 7 consecutive days, with the exception of swimming, bathing, and sleeping time. The accelerometer sampling interval was set at 60-second epochs with a sampling frequency of 30 Hz. Nonwear time was defined by an interval of 60 consecutive minutes of 0 counts per minute, allowing for 2 minutes of nonzero count interruptions. Participants with at least 3 valid days of accelerometer use (2 weekdays and 1 weekend day) and a minimum wear time of 10 hours per day were eligible for inclusion in the data analysis. The Freedson cutoff point was used for categorizing light physical activity (100-1951 counts/minute), moderate to vigorous physical activity (>1951 counts/minute), and sedentary behavior (<100 counts/minute) [ 44 ].

Demographics

The demographic information included age, sex, marital status, education level, residence, monthly income, living situation, and BMI.

Statistical Analyses

The data analyses were performed using IBM SPSS Statistics (version 28.0; IBM Corp) and Mplus 8 (Muthén & Muthén). Data screening and diagnosis tests of data distribution (eg, mean, SD, skewness, and kurtosis) and missing patterns were performed before the descriptive analysis and scale validation. To ensure a reliable estimation for the multidimensional scale, both Cronbach α and composite reliability (CR) coefficients were calculated to evaluate the internal consistency reliability of the eHLS-Web3.0. Additionally, the test-retest reliability was estimated using the intraclass correlation coefficient of pre- and 1-month follow-up data.

The construct validity of the eHLS-Web3.0 was evaluated using CFAs with maximum likelihood estimation. Several goodness-of-fit indices were computed, including robust chi-square ( χ 2 R ), robust chi-square to degrees of freedom ratio ( χ 2 R / df ), comparative-fit index (CFI), Tucker-Lewis index (TLI), root-mean-square error of approximation (RMSEA) and its 90% CI, and standardized root-mean-square residual (SRMR). The following criteria were considered for a satisfactory model goodness of fit: ≤3 for χ 2 R / df , ≥0.9 for CFI and TLI, and ≤0.08 for RMSEA and SRMR [ 45 ].

Convergent validity was assessed by examining the average variance extracted (AVE) and CR for each subscale, with AVE >0.5 and CR >0.7 indicating satisfactory convergent validity for the scale. Concurrent validity was assessed by calculating the zero-order correlations of the eHLS-Web3.0 and its subscales with the eHEALS, adjusted for all demographic confounders. For the discriminant validity, a presumptive 4-factor model incorporating 3 eHLS-Web3.0 subscales and unidimensional eHEALS was estimated in the CFA. The discriminant validity of the subscales was confirmed if the 95% CI of the association between these subscales did encompass the value of 0 and if the Wald chi-square test demonstrated a significant change in model fit after removing a constraint that fixed the factor correlation to zero [ 45 ]. Additionally, structural equation models were performed to assess the predictive validity of the scale by estimating its association with health behaviors, including physical activity and sedentary behavior.

With a sequential model testing approach, multigroup CFA was used to examine the measurement invariance of the eHLS-Web3.0 across gender, education, and residence. Four distinctive levels of measurement invariance were examined by progressively constraining the parameter estimates of the models to be equivalent across the samples: (1) configural invariance, where no parameter estimates were restricted to equality; (2) metric invariance, where factor loadings were constrained to equality; (3) strong invariance, where both factor loadings and item intercepts were constrained to equality; and (4) structural and strict invariance, where all factor loadings, item intercepts, and factor variance and covariance were restricted to equality. The measure invariance was supported if the change in the value of CFI and RMSEA was ≤0.01 and ≤0.015, respectively [ 45 , 46 ].

Descriptive Information of the Study Sample

As outlined in Figure 1 , a total of 642 eligible participants (mean 65.78, SD 3.91 years; 55.8% female) were included in the data analysis, of whom 134 (mean 65.63, SD 3.99 years; 58.2% female) provided valid data at the follow-up assessment. From the diagnostic evaluation, there were no missing data for eHLS-Web3.0 and eHEALS items in the study sample. All the scale items adhered to the normality distribution with absolute values of skewness and kurtosis <1. Descriptive information of the study sample is shown in Table 1 .

a NCD: noncommunicable disease.

b LPA: light physical activity.

c N/A: not applicable.

d MVPA: moderate to vigorous physical activity.

Reliabilities of the eHLS-Web3.0 in the Study Sample

Table 2 shows the mean value, SD, score range, and internal consistency and test-retest reliabilities of the eHLS-Web3.0 in the study sample. Regarding the internal consistency reliability, the eHLS-Web3.0 and its 3 subscales exhibited adequate Cronbach α values (range=0.89-0.94) and CR coefficients (range=0.90-0.97). Regarding the test-retest reliability, 2 time-point measures showed a strong intraclass correlation for the eHLS-Web3.0 and 3 subscales of the eHLS-Web3.0 ( r =0.81-0.91).

Validities of the eHLS-Web3.0 in the Study Sample

For the constructive validity, the results of the CFA showed that the 24-item 3D eHLS-Web3.0 achieved the criteria for good model fit indices in the study sample, with χ 2 R =674.4, df =248, χ 2 R / df =2.72 (<3), CFI=0.952 (>0.9), TLI=0.946 (>0.9), RMSEA=0.052 (90% CI 0.047-0.056; <0.08), and SRMR=0.034 (<0.08). The standardized factor loadings of the eHLS-Web3.0 items ranged from 0.658 to 0.819 (see Table S1 in Multimedia Appendix 1 ).

For concurrent validity, the zero-order correlations between the eHLS-Web3.0 subscales and eHEALS were significant ( r= 0.47-0.76), indicating a satisfactory result, as outlined in Table 3 . The AVE and CR were calculated as 0.58 and 0.97, respectively, using the standardized factor loadings of the items, showing a satisfactory convergent validity of the eHLS-Web3.0.

The latent factor correlations in a proposed 4-factor CFA model (ie, 3 eHLS-Web3.0 subscales and eHEALS) were calculated to evaluate the discriminant validity of the eHLS-Web3.0 subscales and eHEALS in the study sample. The goodness-of-fit indices of the 4-factor model were inferior to those of the original 3-factor eHLS-Web3.0 model, with χ 2 R =2079.8, df =461, χ 2 R / df =4.51, CFI=0.874, TLI=0.865, RMSEA=0.074 (90% CI 0.071-0.077), and SRMR=0.078. Table 4 presents the statistical results of the discriminant validity analysis. Wald chi-square findings were statistically significant in the study sample (all P <.001), and the CIs for each correlation did not contain the value of 0, demonstrating a satisfactory discriminant validity of the eHLS-Web3.0.

In addition, the eHLS-Web3.0 significantly predicted light physical activity ( β =.36, 95% CI 0.19-0.53; P =.004), moderate to vigorous physical activity ( β =.49, 95% CI 0.35-0.62; P <.001), and sedentary behavior ( β =–.26, 95% CI–0.40 to –0.12; P =.002), supporting the predictive validity of the scale for health behaviors. The goodness-of-fit indices indicated a satisfactory result for the 3 models, with χ 2 R =398.3-403.8, df =271, χ 2 R / df =1.47-1.49, CFI=0.949-0.951, TLI=0.944-0.946, RMSEA=0.059-0.060, and SRMR=0.043-0.044.

a P <.001.

a Wald chi-square test: Wald chi-square test constraining the values of the latent interfactor correlations to zero.

b P <.001.

c eHEALS: 8-item eHealth Literacy Scale.

Measurement Invariance of the eHLS-Web3.0 in the Study Sample

Table 5 presents the results of the examination regarding the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence. The configural, metric, strong, and strict models were all shown to have a satisfactory fit to the data for all 3 pairs of subsamples, with ΔCFI <0.01 and ΔRMSEA <0.015. These indices provide support for the invariance of the factorial construct, factor loadings, intercepts, and residual variance of the eHLS-Web3.0 across gender, education level, and residence.

a Chi-square: robust chi-square.

b CFI: comparative fit index.

c ΔCFI: change in the CFI.

d RMSEA: root-mean-square error of approximation.

e ΔRMSEA: change in the RMSEA.

f M0: baseline configural invariance model.

g N/A: not applicable.

h M1: metric invariance model.

i M2: strong invariance model.

j M3: strict invariance model.

Principal Findings

This study aimed to evaluate the reliability, validity, and measurement invariance of the eHLS-Web3.0 for use with older adults who are living with NCDs. In particular, this study examined the internal consistency and test-retest reliabilities, as well as the construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the eHLS-Web3.0 across gender, education level, and residence. Overall, the results from this study suggest that the eHLS-Web3.0 is a reliable and valid tool for measuring eHealth literacy in Chinese older adults with NCDs.

Regarding the reliabilities, analyses of the Cronbach α and CR coefficients indicated adequate internal consistency reliability for both the eHLS-Web3.0 and its 3 subscales. These findings are consistent with previous research on the use of the eHLS-Web3.0 among Chinese young adults [ 33 ]. It is worth noting that while previous studies have generally supported the reliability of the eHEALS and other eHealth literacy assessments (eg, DHLI and C-eHEALS) among older adults or those with NCDs [ 2 , 27 , 28 ], the novel eHEALS-Web3.0 tool has not yet been evaluated for reliability in older populations. This study is the first to investigate the reliability of the eHEALS-Web3.0 among older adults with NCDs. Additionally, previous psychometric analyses of eHealth literacy measures have primarily focused on internal consistency reliability, with test-retest reliability often overlooked [ 19 , 27 , 28 ]. In contrast, this study further examined the test-retest reliability of the eHLS-Web3.0 and its subscales, and the findings demonstrated a strong cross-time stability for the scale, as evidenced by a significant correlation between baseline and 1-month follow-up measures.

Regarding the construct validity, the CFA results provided support for the 3D model structure of the eHLS-Web3.0 among Chinese older adults with NCDs. The acquisition and verification subscales of the eHEALS-Web3.0 assess individuals’ eHealth abilities in Web1.0 and Web2.0 contexts, similar to the eHEALS and second-general eHealth literacy measures [ 12 , 19 , 27 ]. However, the eHEALS-Web3.0 stands out by also evaluating individuals’ proficiency in applying eHealth information to evolving health needs in the Web3.0 era (ie, the application subscale). As the digital landscape advances, individuals have more opportunities and options to use eHealth information. For instance, they can use eHealth information to make informed health decisions or resolve health-related problems, create their own health data, monitor their health status, interact with others, exchange information, and provide health advice to other health information seekers [ 31 , 33 ]. The 3D eHLS-Web3.0 provides a comprehensive assessment of eHealth literacy, catering to the present digital circumstances.

For other validities of the eHLS-Web3.0, the concurrent validity was confirmed by a significant correlation between the eHLS-Web3.0 and its subscales with the eHEALS, while the AVE and CR supported the convergent validity of the scale. In addition, as the 3 eHLS-Web3.0 subscales and eHEALS differ in conceptual content, a 4-factor model integrating the acquisition, verification, and application subscales and unidimensional eHEALS was established to confirm the discriminant validity of the scale. Although the Wald chi-square test results supported the discriminant validity of the eHLS-Web3.0, a high correlation was observed among the latent factors in the 4-factor model, possibly due to measurement errors [ 47 ]. To validate the earlier findings, zero-order correlations were calculated using composite (averaged) scales. Fortunately, the overall results confirmed the discriminant validity of the scale. Finally, the predictive validity of the eHLS-Web3.0 was supported by a significant positive association between the eHLS-Web3.0 and physical activity, as well as a negative association with sedentary behavior. Previous studies have demonstrated a positive association between eHealth literacy and health-promoting behaviors (eg, physical activity) among diverse populations [ 13 , 37 , 48 ]. However, there is a lack of evidence on the relationship between eHealth literacy and risk behaviors (eg, sedentary behavior). Our findings underline the potential of including eHealth literacy as a modifiable factor in future eHealth interventions to facilitate health behaviors and improve health outcomes among older adults with NCDs.

For measurement invariance, the establishment of configural, metric, strong, and strict invariances demonstrated that the eHLS-Web3.0 is a psychometrically sound instrument for measuring eHealth literacy among Chinese older adults with NCDs, regardless of their gender, education level, and residence. These invariances provide a solid foundation for making appropriate and meaningful transgroup comparisons in future studies.

Limitations and Implications

Some limitations should be noted. First, the nonrandom sampling used in this study may have limited the representativeness of the study findings. Therefore, a stratified random sampling approach is warranted in future studies. Second, given that the study findings are based only on the sample of Chinese older adults with NCDs, one should be cautious when generalizing these results to other samples. Future studies should examine the psychometric properties of the scale across different populations and diverse cultural contexts. Moreover, self-reported measures may result in some response biases (eg, recall bias and social desirability); therefore, the inclusion of objective means for assessing eHealth literacy should be considered in the future. Additionally, it is worth exploring the prediction of eHealth literacy on other health outcomes and examining its underlying mechanisms. Finally, from a pragmatic perspective, it may be beneficial to develop and validate a brief version of the eHLS-Web3.0, particularly with regard to older populations who may struggle with completing lengthy self-reported scales.

Despite the aforementioned limitations, this study addresses a significant gap in the literature by validating and applying the eHLS-Web3.0, a specific measure of eHealth literacy used for Chinese older adults with NCDs in the Web3.0 landscape. Previous reviews have revealed a wide range of influential factors of eHealth literacy as well as a positive correlation between higher eHealth literacy and better health behaviors, knowledge, and attitudes in older adults [ 38 , 49 - 51 ]. These findings indicate the potential for developing eHealth literacy interventions to promote positive health behaviors in the future while considering various socioeconomic and cultural variables. However, previous studies have yielded conflicting results regarding certain physical and psychosocial outcomes [ 38 ], underlining the need for more high-quality research. It is important to note that the success of these efforts largely depends on a reliable and accurate assessment of eHealth literacy [ 52 ].

The findings of this study provide robust support for the reliability, validity, and measurement invariance of the eHLS-Web3.0, indicating that this up-to-date tool can be widely used in future research endeavors to appropriately and accurately assess older adults’ abilities to search for, retrieve, evaluate, and use web-based health resources. This advancement has the potential to significantly contribute to both the field of eHealth literacy research and the development of targeted health promotion programs in the future. As digital technology increasingly infiltrates the health care sector, promoting eHealth literacy among older adults is more critical than ever [ 52 , 53 ]. The development and validation of the eHLS-Web3.0 marks a significant milestone in the field of eHealth literacy research, serving as a necessary foundation for future empirical investigations and targeted interventions aimed at improving eHealth literacy among older adults.

Conclusions

To the best of our knowledge, this is the first study to examine the psychometric properties and measurement invariance of the eHLS-Web3.0 among Chinese older adults with NCDs. This study provides evidence for internal consistency and test-retest reliabilities, construct, concurrent, convergent, discriminant, and predictive validities, and the measurement invariance of the 24-item 3D eHLS-Web3.0 for use with Chinese older adults with NCDs. The eHLS-Web3.0 can serve as a psychometrically sound instrument for assessing eHealth literacy in the Chinese context.

Acknowledgments

This research was funded by the Humanities and Social Science Fund of Ministry of Education of China (23YJCZH121), as well as the Humanities and Social Sciences Revitalization Grant of Shenzhen University (WKZX0312). The funding organizations had no role in the study design, study implementation, manuscript preparation, or publication decision. This work is the responsibility of the authors.

Authors' Contributions

WL, WC, and HL conceived and designed the study. HZ, JZ, RZ, and LZ contributed to the preparation of the study materials. WL, WC, NS, HZ, YY, and LZ collected the data. WL screened and analyzed the data. WL and WC drafted and revised the manuscript. All authors have reviewed and approved the final version of the manuscript.

Conflicts of Interest

None declared.

Standardized factor loadings of the 24-item eHealth Literacy Scale-Web3.0.

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Abbreviations

Edited by T de Azevedo Cardoso; submitted 04.09.23; peer-reviewed by W Yu, S Jiang, B Shang; comments to author 21.11.23; revised version received 11.12.23; accepted 19.04.24; published 03.06.24.

©Wenfei Cai, Wei Liang, Huaxuan Liu, Rundong Zhou, Jie Zhang, Lin Zhou, Ning Su, Hanxiao Zhu, Yide Yang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.06.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

The state of customer care in 2022

Customer care leaders are facing a perfect storm of challenges: call volumes are up, employees are leaving and harder to replace, and digital solutions aren’t yet delivering on their full promise. Add rising customer expectations and decades-high inflation  to the mix, and it’s easy to understand why customer care leaders are feeling the pressure.

About the authors

This article is a collaborative effort by Jeff Berg , Eric Buesing , Paul Hurst, Vivian Lai, and Subhrajyoti Mukhopadhyay, representing views of McKinsey’s Customer Care service line.

The stakes couldn’t be higher as teams try to adapt to a postpandemic era of customer care. Over the past two years, leaders have had to quickly adapt systems and ways of working to accommodate the shift to working from home—up to 85 percent of their workforces, in some cases. Contact center employees are harder to hold onto, and nearly half of customer care managers experienced increased attrition in 2021, leading to performance variability.

Over the past two years, customer care leaders have had to quickly adapt systems and ways of working to accommodate the shift to working from home.

While digital solutions and the shift to self-service channels will solve many of these challenges, they aren’t quite reaching the goal. For most organizations, the vast majority of digital customer contacts require assistance, and only 10 percent of newly built digital platforms are fully scaled or adopted by customers.

Not surprisingly, McKinsey’s 2022 State of Customer Care Survey has found that customer care is now a strategic focus for companies. Respondents say their top three priorities over the next 12 to 24 months will be retaining and developing the best people, driving a simplified customer experience (CX)  while reducing call volumes and costs, and building their digital care and advanced analytics ecosystems.

With challenges on all fronts, the question now confronting leaders is how best to prioritize investment across the people, operations, and technology aspects of their customer care strategies. Knowing where to focus or what to do first isn’t easy, and businesses need to move quickly. Companies that don’t invest in this area face the possibility of further talent attrition, customer dissatisfaction, and even loss of market share.

But customer care is also now a major opportunity for businesses. Done well—through a combination of tech and human touch—it is an area where companies can drive loyalty through a more personalized customer journey while unlocking greater productivity, increased revenue, improved job satisfaction, and real-time customer insights.

This article presents the key findings of the 2022 State of Customer Care Survey and how businesses are shifting priorities at this critical time.

Challenges on all fronts

To uncover the latest trends in customer care, McKinsey surveyed more than 160 industry leaders and experts at the director, senior director, vice president, and C-suite levels to find out how their operations have been affected over the past two years of the COVID-19 pandemic.

Care is at an inflection point

The survey findings indicate that customer care is at an inflection point. Call volumes are higher and more complex than before, while companies find themselves struggling to find talent and train them to proficiency at pace.

As customer care increasingly moves online, the distinction between digital and live interactions has also begun to blur. Organizations are looking for new capabilities that will enhance both the customer and employee experience in “moments that matter”—those interactions that may have previously happened face to face or have significant influence on overall CX.

Compared with results of the 2019 State of Customer Care Survey, customer care leaders are now more focused on improving CX, reducing contact volumes, deploying AI assistance, and increasing revenue generation on service calls (Exhibit 1).

Customer care talent is increasingly scarce

Higher call volumes and more complex calls are challenging existing capacity—61 percent of surveyed care leaders report a growth in total calls, with increased contacts per customer and a growing customer base as the key drivers. And 58 percent of care leaders expect call volumes to increase even further over the next 18 months.

While a growing customer base is a positive sign for business, it puts greater pressure on contact centers that are already under strain. More customers mean increased call numbers, and with more complex calls, customers tend to have to phone contact centers over and over again—further affecting capacity and resulting in a more negative CX overall.

To make matters worse, talent attrition is affecting customer care capacity. Employees are leaving faster than they did before the pandemic—a result of the Great Attrition—and are more difficult to replace. Nearly half of surveyed managers report increased employee attrition over the past 12 months.

The top-cited reason for employees leaving is poaching by competitors—58 percent—alongside employee burnout, employee dissatisfaction, lack of advancement opportunities, and poor work–life balance (Exhibit 2).

Retaining talent could prove vital in the race to maintain capacity. New hires require significant staff training, with 41 percent of surveyed leaders reporting that it takes between three and six months to train a new employee for optimal performance and a further 20 percent saying it takes more than six months.

Uniting self-service and live channels

Many companies have made significant investments in digital care capacity in recent years, though cross-channel integration and migration issues continue to hamper progress. For example, 77 percent of survey respondents report that their organizations have built digital platforms, but only 10 percent report that those platforms are fully scaled and adopted by customers. Only 12 percent of digital platforms are highly integrated, and, for most organizations, only 20 percent of digital contacts are unassisted.

In an increasingly digital first environment, however, customer care is fundamental to how organizations interact with their customers. Leaders in this field are asking, “How do we create a better, more personalized experience through digitally enabled services?”

Businesses are investing in three critical areas

Faced with the challenges of a fast-changing and demanding environment, companies can’t afford to refrain from acting on the customer care storm. Over the past two years, customers have flocked to digital channels because of the pandemic, and organizations have had to race to meet their needs with new channels that support remote and digital transactions.

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In a postpandemic future, this pivot to digital is likely to keep growing. And while many companies believe that they have made significant strides in their customer care transformation journey, a significant number remain at a foundational level—they are improving self-service options and automating common requests but haven’t yet moved far enough along the journey to distinguish from their competitors. Meanwhile, those that have the leading edge are leveraging real-time customer behavior insights and conversational AI to deliver proactive customer outreach.

Customer care leaders say their top three priorities over the next 12 to 24 months are to retain and develop the best people, drive a simplified CX while reducing call volumes and costs, and build out their digital care ecosystems.

Retain and develop the best people

Traditionally, customer care talent has been regarded as cheap, easy to replace, and relatively low skilled. But with call volumes growing and calls becoming more complex to resolve, these employees now require more strategic consideration.

With three out of five surveyed leaders citing attracting, training, and retaining talent as a top priority, businesses are looking at ways to build a better organizational culture. Two of the most effective ways to do this—according to customer care leaders—are to find ways to motivate and build trust with employees and to encourage leaders to listen and act on employee feedback (Exhibit 3).

Shift the interactions

Shifting the workload away from transactional, repetitive calls can address a number of the headaches facing customer care leaders. The move can free up capacity to improve CX while offering more rewarding work to employees.

Companies are looking to shift from a transactional to a solution-oriented interaction during the live, complex calls that matter most to customers. Organizations are also turning to self-service channels and tech to resolve high volumes. And the strategy is working. Nearly two-thirds of those surveyed that decreased their call volumes identified improved self-service as a key driver (Exhibit 4).

Organizations are planning to increase digital interactions one and a half times by 2024. The top three areas identified for investment include tech that improves omnichannel and digital capabilities—for example, chatbots and AI tools—automated manual activities in contact centers, and advanced analytics capabilities.

Despite digital tech taking on more of the burden for customer service interactions, human assistance will likely remain an important driver of overall CX, especially in the moments that matter. Customers want fast, efficient service, but they also want personalized customer care, whatever the channel of engagement.

Develop AI-powered customer care ecosystems

The growing challenges around increasing volumes, rising complexity, and limited talent availability are unlikely to be solved at scale without AI and data analytics. Companies can optimize the entire customer operations footprint by using tech to measure performance, identify opportunities, and deploy value-capturing change management, thus delivering critical operations insights and impact at scale.

For customers, AI-driven tools like predictive analytics can deliver a personalized and proactive experience that resolves issues before customers are even aware that they exist—enhancing CX at every point along the customer journey. Tech can also assist in developing a high-performing workforce by identifying optimal work processes and practices using analytics. Automated coaching can potentially be deployed to every individual, supporting efforts to attract, develop, and retain scarce talent.

How CEOs can win the new service game

In the AI-powered care ecosystem, around 65 percent of tasks and 50 to 70 percent of contacts are automated, creating a true omnichannel experience that provides a consistent and seamless experience across interactions. In this way, the potential of contact centers could be unlocked to become loyalty-building revenue generators through greater solutioning and sales excellence.

Putting priorities into practice

CX is fast becoming a key competitive area. Companies that don’t prioritize their strategy and digital transformation journeys are likely to face continued customer dissatisfaction, as well as talent attrition—thus threatening their brand and market competitiveness.

Getting customer care right depends on prioritizing and investing across the people, operations, and tech aspects of the customer care strategy. Companies can consider the following key steps as they look to build out their capabilities and invest in their digital care ecosystems:

• Start by setting out the vision for the customer care organization, capturing what excellence looks like.
• Conduct a rapid but thorough due-diligence-style assessment of people, processes, and capabilities, looking at the customer care operation in a new light to identify not just incremental changes but a reimagined, large-scale transformation.
• Path one follows a traditional design approach, which may take longer but prove less risky, as the entire transformation is considered at the outset.
• Path two involves an interactive and agile design, test, and iterate methodology, which may lead to new solutions quickly.
• Leverage the full suite of available technologies and analytical approaches that are driving successful outcomes in customer care, including natural language processing (NLP) and AI in frontline operations to match work to workers, together with cognitive AI assistance for resolving simpler customer queries.

Personalized digital interaction nowadays is an expectation rather than a luxury or an added perk, and customer care is the issue at the heart of this digital first environment—companies can’t afford to stumble at this juncture. If done well, however, customer care presents a great opportunity to build loyalty and long-term relationships with customers, creating organizational resilience for the future.

Jeff Berg is a partner in McKinsey’s Southern California office; Eric Buesing is a partner in the Stamford, Connecticut, office; Paul Hurst is a consultant in the Charlotte, North Carolina, office, Vivian Lai is a consultant in the New York office, and Subhrajyoti Mukhopadhyay is an expert in the Chicago office.

The authors wish to thank Karunesh Ahuja and Charles-Michael Berg for their contributions to this article.

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Spending too much time on social media and doomscrolling? The problem might be FOMO

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For as long as we have used the internet to communicate and connect with each other , it has influenced how we think, feel and behave.

During the COVID pandemic, many of us were “cut off” from our social worlds through restrictions, lockdowns and mandates. Understandably, many of us tried to find ways to connect online .

Now, as pandemic restrictions have lifted, some of the ways we use the internet have become concerning. Part of what drives problematic internet use may be something most of us are familiar with – the fear of missing out, or FOMO.

In our latest research , my colleagues and I investigated the role FOMO plays in two kinds of internet use: problematic social media use and “doomscrolling”.

What are FOMO, problematic social media use and doomscrolling?

FOMO is the fear some of us experience when we get a sense of “missing out” on things happening in our social scene. Psychology researchers have been studying FOMO for more than a decade , and it has consistently been linked to mental health and wellbeing , alcohol use and problematic social media use .

Social media use becomes a problem for people when they have difficulty controlling urges to use social media, have difficulty cutting back on use, and where the use has a negative impact on their everyday life.

Doomscrolling is characterised by a need to constantly look at and seek out “bad” news . Doomscrollers may constantly refresh their news feeds or stay up late to read bad news.

While problematic social media use has been around for a while, doomscrolling seems to be a more recent phenomenon – attracting research attention during and following the pandemic.

What we tried to find out

In our study, we wanted to test the idea that FOMO leads individuals to engage in problematic use behaviours due to their difficulty in managing the “fear” in FOMO.

The key factor, we thought, was emotion regulation – our ability to deal with our emotions. We know some people tend to be good at this, while others find it difficult. In fact, greater difficulties with emotion regulation was linked to experiencing greater acute stress related to COVID .

Read more: Why am I online? Research shows it's often about managing emotions

However, an idea that has been gaining attention recently is interpersonal emotion regulation . This means looking to others to help us regulate our emotions.

Interpersonal emotion regulation can be helpful (such as “ affective engagement ”, where someone might listen and talk about your feelings) or unhelpful (such as “ co-rumination ” or rehashing problems together), depending on the context.

In our analyses, we sought to uncover how both intrapersonal emotion regulation (ability to self-manage our own emotional states) and interpersonal emotion regulation (relying on others to help manage our emotions) accounted for the link between FOMO and problematic social media use, and FOMO and doomscrolling, respectively.

What we found – and what it might mean for the future of internet use

Our findings indicated that people who report stronger FOMO engage in problematic social media use because of difficulty regulating their emotions (intrapersonally), and they look to others for help (interpersonally).

Similarly, people who report stronger FOMO are drawn to doomscrolling because of difficulty regulating their emotions intrapersonally (within themselves). However, we found no link between FOMO and doomscrolling through interpersonal emotion regulation.

We suspect this difference may be due to doomscrolling being more of a solitary activity, occurring outside more social contexts that facilitate interpersonal regulation. For instance, there are probably fewer people with whom to share your emotions while staying up trawling through bad news.

While links between FOMO and doomscrolling have been observed before, our study is among the first to try and account for this theoretically.

We suspect the link between FOMO and doomscrolling may be more about having more of an online presence while things are happening . This would account for intrapersonal emotion regulation failing to help manage our reactions to “bad news” stories as they unfold, leading to doomscrolling.

Problematic social media use, on the other hand, involves a more complex interpersonal context. If someone is feeling the fear of being “left out” and has difficulty managing that feeling, they may be drawn to social media platforms in part to try and elicit help from others in their network.

Getting the balance right

Our findings suggest the current discussions around restricting social media use for young people , while controversial, are important. We need to balance our need for social connection – which is happening increasingly online – with the detrimental consequences associated with problematic internet use behaviours.

It is important to also consider the nature of social media platforms and how they have changed over time. For example, adolescent social media use patterns across various platforms are associated with different mental health and socialisation outcomes.

Public health policy experts and legislators have quite the challenge ahead of them here. Recent work has shown how loneliness is a contributing factor to all-cause mortality (death from any cause).

Read more: Doomscrolling is literally bad for your health. Here are 4 tips to help you stop

We have long known, too, that social connectedness is good for our mental health . In fact, last year, the World Health Organization established a Commission on Social Connection to help promote the importance of socialisation to our lives.

The recent controversy in the United States around the ownership of TikTok illustrates how central social media platforms are to our lives and ways of interacting with one another. We need to consider the rights of individuals to use them as they please, but understand that governments carry the responsibility of protecting users from harm and safeguarding their privacy.

If you feel concerned about problematic social media use or doomscrolling, you can speak to a healthcare or mental health professional. You can also call Lifeline on 13 11 14, or 13 YARN (13 92 76) to yarn with Aboriginal or Torres Strait Islander crisis supporters.

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• Internet use
• Problematic Internet use
• Social media use
• Fear of missing out
• doomscrolling
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Forced labour is a severe violation of human rights affecting 28 million of men, women and children in all countries and all economic sectors. It is rooted in poverty, discrimination and lack of social protection, and it disrupts fair competition between businesses. The issue has been at the heart of the ILO mandate to promote Fundamental Principles and Rights at Work, leaving no one behind.

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The Forced Labour Convention (No. 29), adopted in 1930, contains the definition of forced labour and provides that it should be punished as a crime. This is one of the most ratified ILO standards. 

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The Abolition of Forced Labour Convention (No. 105), adopted in 1957,  deals with state-imposed forms of forced labour. This is one of the most ratified ILO standards. 

The Protocol to the Forced Labour Convention, (P029), adopted in 2014, requires ratifying countries to take effective measures to prevent forced labour, protect victims and ensure their access to justice. 

The Protocol complements the Convention No. 29, therefore only countries that have ratified this Convention can ratify the Protocol. 

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The Forced Labour Recommendation (No. 203), adopted in 2014, provides further guidance on how to implement the Protocol.

It is a non-binding document that does not require ratification. 

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