Margaret, thank you for taking part in this interview and sharing your insights with us. Many of us are familiar with your work on qualitative research design and analysis. Could you share what initially drew you to qualitative research?

When I completed graduate school with a degree in psychology, I was confident in what I wanted to do for the rest of my life in terms of a career. I knew that I wanted to be a “researcher.” I put “researcher” in quotes because I had my own definition of what it meant to be a researcher. To me, it meant that I not only was knowledgeable and comfortable in quantitative research but also in qualitative methods and design. Graduate school left my head full of knowledge about experimental and survey research but I knew absolutely nothing about qualitative research. I knew that to be a researcher I needed qualitative research expertise but I had no training or knowledge about it.

My first instinct was to go back to school with a focus on qualitative research. But in those days, that was definitely not an option as there were no graduate programs with this focus. So, I turned away from pursuing a PhD and set my sights on an applied approach to gaining the knowledge I desperately wanted by actively looking for a job that would allow me to work as a trainee. I found that job at the Los Angeles Times in LA. At the time, the LA Times conducted a great deal of focus group discussions for their advertisers and they wanted to hire someone to train under their current focus group moderator. The idea was that the trainee would become on par with the current moderator and share the focus group workload. And indeed, that is what happened. After months of studying video recordings of past discussions in their video library, playing the role of in situ videographer, helping with recruitment, and conducting mock discussions with LA Times employees, I was promoted to focus group moderator for the paper.

That experience taught me a great deal about the focus group method and about qualitative research more generally. From there, I moved to San Francisco and worked for an AT&T company as a research manager of survey and qualitative research.

Drawing from your experience, what are some common challenges you’ve encountered in designing, conducting, and analyzing qualitative research? What strategies have you found most helpful in navigating these challenges?

One of the biggest challenges – and a challenge that hasn’t changed over the decades – is associated with the fact that qualitative research takes time. Regardless of method or mode, qualitative research requires devoted attention by the researcher to each aspect of the research. One reason that the factor of time is such a challenge is because research sponsors are often unfamiliar with qualitative research and, more specifically, with what is required for a quality approach.

For example, sampling in qualitative research typically follows a purposive design whereby the researcher looks for specific types of participants to meet the research objectives; however, finding participants can be a real challenge when the population segment of interest is very small. Once the appropriate participants have been identified, recruiting participants becomes another challenge. Regardless of mode, there are many reasons why individuals may not agree to participate or may agree but not show up on the scheduled date. A decades old but still apt example is when we were conducting focus group research at the Los Angeles Times with men who had recently undergone hair transplants and only one person showed up on the night of the discussion. We asked this person to speculate why the others did not cooperate with our request. It was from that conversation that we made a second attempt at a focus group discussion but doubled the amount of the cash incentive and over recruited more than normal. Ah ha! Everybody who was recruited showed up for the focus group which we now had to split into two discussion groups due to the number of participants. And data analysis, of course, is very time intensive. All the software and artificial intelligence (AI) in the world can’t replace the necessary time the researcher takes (or should take) to absorb the data – participant by participant, group discussion by group discussion – and embrace the contextual nuances (both verbal and nonverbal) that are utilized in the analysis. Qualitative researchers owe it to themselves and to the sponsors of their research to communicate upfront about the time requirements for all phases of the research, emphasizing the benefit of this approach that will ultimately lead to useful outcomes.

Another challenge goes back to something I already mentioned; that is, working with research sponsors who are less knowledgeable about qualitative research and the quality design considerations. For example, clients may ask me to add questions to a focus group discussion that I believe are inappropriate considering the research objectives and/or method and/or mode. A major automotive client once asked me to ask group participants to name a Hollywood celebrity that fits their image of a particular car model. When I couldn’t convince the client that this was a time waster and would divert us from the key objectives, I went ahead and did what I was asked, which had the effect of showing the client the folly of this projective technique. Clients may also be less knowledgeable about ethical considerations. For example, I had a client ask me to divulge the names of the 28 individuals I interviewed across the U.S. for an in-person, in-depth interview study. I refused and the case went to court, where the judge agreed with me and the problem went away. In the end, it is up to the qualitative researcher to educate the research sponsor on qualitative research and the research principles that will be followed throughout the study. And, when these principles are compromised, it is up to the researcher to stand their ground and uphold their quality approach.

The landscape of survey research has seen important changes over the last few decades, including shifts in contact and data collection modes, the rise of online panels, and changes in data analysis software. From your perspective, what have been the most impactful changes in qualitative research?

The internet, of course, has had a significant impact on the qualitative researcher’s mode options. In the past, in-person and phone modes were the only ones that were widely used. Regardless of method, qualitative researchers have relied on these two modes. That all changed when the internet introduced online options, including video in-depth interviews (IDIs) and focus group discussions, as well as asynchronous research such as “bulletin boards” or group discussions that are conducted over a three-day or longer time period. This has enabled qualitative researchers to design broader, more inclusive, studies within a reasonable use of resources, i.e., time and money. Having said that, a shift to the online mode has, in my opinion, potentially distanced qualitative researchers from their participants. I have conducted many in-person IDIs, typically about 30 in-person interviews for any given study. These interviews have taken me across the country and allowed me to meet with participants at a location of their choosing. The intensity and contextual richness of these IDIs – as well as the extended length of these interviews which often comes naturally in the in-person mode – is in profound contrast to conducting IDIs in the online mode.

Computer assisted qualitative data analysis software (CAQDAS) is another game changer. With CAQDAS, qualitative researchers can develop a systematic and efficient analysis process, particularly in the area of coding. Beyond coding, CAQDAS aids in literature reviews, automated transcriptions, social media analysis, analysis of video content, and data visualization. Like everyone else, CAQDAS providers have jumped on board with AI which has augmented their service capabilities. Although CAQDAS can be very useful, it is simply a tool. It is the researcher’s total immersion in the data that ultimately reaps useful interpretations and next steps.

Technology, particularly AI, continues to transform many aspects of life, and research is no exception. What are your thoughts on the impact of AI on qualitative data collection and analysis now and in the future?

AI has permeated the life we live and the research we conduct. This is certainly true in qualitative research where researchers are inundated with the latest integration of AI-enabled tools from providers of sampling, recruiting, data collection, and analysis. Fortunately, there is a great deal of discussion about AI in qualitative research, including a webinar in October 2023 hosted by the AAPOR qualitative affinity group QUALPOR, as well as a conversation between Andrew Stavisky and Darby Steiger on “Perspectives on AI in Qualitative Research” published in the April 2024 QUALPOR newsletter. These and other AI-related discussions provide a valuable resource to everyone who is working to stay informed and knowledgeable about how (or if) to use AI in their research designs.

I believe there is a place for AI in qualitative research designs to the extent that AI is facilitating processes but not dominating the thinking directly related to the data itself. This includes some of the areas that Darby Steiger mentions in the conversation cited earlier, such as literature reviews, recruitment (e.g., identifying potential participants and pretesting screeners), interview/discussion guide development, and translation. These are areas where AI holds the promise of making our lives easier while also alerting us to other directions to consider in recruitment and data collection.

The problem arises when we allow AI to shut off our thinking. As qualitative researchers, we are committed to the “humanness” of the qualitative approach requiring a profound involvement in the research process. So, AI may help to spark new ideas for the interview guide but it is the researcher who needs to write the guide and give thoughtful consideration to suggested additions or subtractions. AI may have the capacity to “moderate” a focus group discussion but removing the researcher from the essence of data collection, and the thinking process to collect the data, is nonsensical. As far as analysis, allowing AI to take over code development and coding potentially weakens the researcher’s ability to think deeply about the contextual meaning of the codes and hence what is being learned about the research objectives. This less-than-profound understanding of the code structure becomes a significant problem when the researcher takes the next step to derive categories of constructs and, from categories, themes.

So, the potential of AI to supplant the researcher’s deliberate dedication to fully immersing themselves in the thinking process is one aspect of AI to consider. Another has to do with the usefulness of AI when it comes to nonverbal data. Here, I am talking about participants’ emotions and gestures as well as stimuli often used in qualitative research. For instance, many of the studies that I have worked on have had something to do with communication, i.e., asking participants to respond to various documents or communication pieces to gain feedback on specific aspects of these materials and their ability to communicate the intended directive or message. In one study for a financial services provider, I asked focus group participants to examine mortgage documents and discuss the clarity of the sections requiring the borrower’s input. Here, the nonverbal data consisted of the many and various nonverbal cues made by the participants as they pored through and reacted to the stimuli as well as the documents themselves. I have also conducted a great deal of employee research when the objective is to gain responses on a wide range of internal communications that are shown in an interview or group discussion. Nonverbal input may also include the moderator’s notes or “scribblings.” For instance, when I moderate a focus group I often use the easel or whiteboard to jot down what I am hearing from participants. I then use these notes to spur more discussion on a topic area. As the researcher, I am aware of the manifest and underlying meaning of these scribblings and consider them in the analysis but I do not transcribe them because my “scribbles” would lose meaning when doing so.

Speaking of analysis, an important and often overlooked component of analysis when considering AI is the researcher’s orientation or philosophical perspective. Whether researchers consciously know it or not, we all come to our research with a way of thinking about our data and how we approach analysis. My thinking, for example, is oriented around social constructionism and interpretative phenomenological analysis where the emphasis is on finding meaning of lived experiences derived from a shared context. Other researchers may harbor a more postpositivist viewpoint. Others may work in the area of critical theory that focuses on social change and culture, utilizing a collaborative approach such as participatory action research. These paradigm orientations are at the core of our work and yet I have not read anything that would suggest that AI is trained in this manner.

Another important consideration when thinking about incorporating AI is an ethical one. As researchers who work closely with participants to gain their cooperation and insights, we owe it to them to maintain the integrity of their contribution. Beyond data protection and privacy, participants deserve a rich, contextually driven analysis of their data that is not compromised by a technological solution.

And finally, a discussion of AI is not complete without mentioning something I talked about earlier. That is, time. And the importance of allocating sufficient time in qualitative research design. It is difficult (impossible?) to read anything about AI and qualitative research without reading about the virtue of AI as a time saver. My advice to researchers: If saving time is the reason you choose to use AI in your qualitative studies, you need to rethink whether qualitative research is the best approach. As I say in this article concerning the idea that time is a key ingredient in qualitative research methodology and particularly in analysis, “Qualitative data analysis — understanding the contextual meanings of how people think (individually and collectively) — takes time. Embrace it. Enjoy it. It is why we conduct qualitative research in the first place.”

For those just starting their journey in qualitative or mixed-methods research, what advice would you offer?

I have no regrets for the choices I made in my career and, for that reason, would encourage early career researchers to consider a similar path. Here are a few ideas:

  1. Begin by gaining knowledge of basic research principles. Ground yourself in what it means to conduct “good” research regardless of method. For example, make deliberate choices in school to take general courses such as Research Design and Methods. If you are out of school, look for opportunities to enroll in academic courses to gain this knowledge.

  2. Give deep consideration and introspect on where your interests lie. For instance, I began in college as a math major but after two years I realized something was missing. I reflected long and hard on this and determined that what was missing (for me) was the human element. I needed psychology, where I could pursue my interest in research while combining it with a closer human connection.

  3. Deliberately look for an employment opportunity that will allow you to learn and grow in keeping with the path you have laid out for yourself.

  4. Learn from your encounters with research sponsors or other members of your research team. What is their definition of qualitative or mixed methods research? How does that compare with your definition and your understanding of research design? What can you learn from them, and what can they learn from you?

  5. Join and get involved with professional organizations, such as the American Association for Public Opinion Research, American Sociological Association, American Political Science Association, Society for Qualitative Inquiry in Psychology (a division in APA), QRCA, and Mixed Methods International Research Association; and, at the least, attend the meetings to expand your knowledge and your network.

  6. Sign up for newsletters and forums/listservs that are compatible with your interests and will inform you of opportunities as well as stretch your thinking.

  7. Take advantage of the many online webinars, courses, and other sessions that will add to your knowledge and keep you up to date on what others are doing related to your area of interest.