My Experience Using Qualitative Research Methods During My EdD in Public Health

My Experience Using Qualitative Research Methods During My EdD in Public Health

Reflections on qualitative research design, participant recruitment, thematic analysis, and studying artificial intelligence in higher education.

When I began developing my Doctor of Education (EdD) dissertation in Public Health, I chose to use qualitative research methods to investigate how undergraduate public health students experienced and perceived artificial intelligence (AI) in their coursework.

My dissertation, titled AI Use by Public Health Undergraduates in 100-Level Courses: A Qualitative Exploratory Case Study, focused on understanding how students incorporated AI tools into their academic work, what benefits they perceived, and what concerns they had about the technology's influence on learning.

Unlike quantitative research, which generally focuses on measuring variables and examining numerical relationships, qualitative research allows researchers to investigate experiences, perceptions, and the meanings individuals assign to them.

This distinction was particularly important for my study. I was not simply interested in determining how many students used AI or how frequently they used it. I wanted to understand how students made decisions about AI, how they evaluated its usefulness, and whether they believed it influenced their learning.

Conducting the study gave me an opportunity to explore the practical challenges of qualitative research, from developing open-ended questions to identifying themes across participant responses.

Choosing a Qualitative Research Design

One of the first decisions was selecting a research design that aligned with my research questions.

Because I wanted to explore students' experiences with AI within the context of introductory public health education, I selected a qualitative exploratory case study.

A case study approach allowed me to investigate a defined educational context while examining multiple aspects of the phenomenon.

Rather than attempting to establish causal relationships or test statistical hypotheses, I focused on understanding how students described their experiences.

This required a different approach to research planning than I would use for a quantitative study.

In quantitative research, investigators typically identify variables, select measurement instruments, and establish statistical analyses before collecting data. Although qualitative studies also require careful planning, their emphasis is on collecting sufficiently detailed information to understand participants' perspectives.

I needed to develop questions that would encourage students to describe their experiences rather than simply confirm assumptions I already had about AI use.

This was especially important because AI in education is a subject that can generate strong opinions among students, educators, and institutions.

Developing the Theoretical Framework

My dissertation incorporated Adult Learning Theory and Rational Choice Theory to help interpret students' decisions and experiences.

Adult Learning Theory was relevant because undergraduate students are expected to take increasing responsibility for their educational activities. This includes determining how to study, when to seek assistance, and which resources to use when completing assignments.

AI tools introduce another resource that students can incorporate into these decisions.

Rational Choice Theory provided an additional perspective by considering how students might evaluate the perceived benefits and costs of using AI.

For example, a student might use AI because it reduces the time required to complete an assignment or helps explain unfamiliar material. Another student might avoid it because of concerns about inaccurate information, academic integrity, or becoming dependent on the technology.

These theories helped establish a conceptual foundation for examining student decision-making.

However, an important consideration in qualitative research is ensuring that the theoretical framework does not predetermine the findings.

I needed to remain open to experiences that did not fit neatly into the theoretical perspectives I had selected.

The theories were intended to support interpretation rather than dictate what participants should report.

Developing Open-Ended Research Questions

One of the most important parts of the research process was developing the data collection instrument.

My study used 25 open-ended questions designed to explore different aspects of students' experiences with AI.

These questions addressed how students used AI tools, their perceived benefits, concerns about academic learning, and their understanding of AI's role in education.

Developing open-ended questions required careful attention to wording.

A question such as "Does AI negatively affect your learning?" introduces a particular interpretation before the participant has an opportunity to describe their experience.

A more neutral question asks participants to explain whether and how AI has influenced their learning.

This distinction is important because qualitative researchers must avoid unnecessarily leading participants toward predetermined conclusions.

I also had to consider how much detail participants might provide.

Unlike an interview, where the researcher can immediately ask follow-up questions, a written questionnaire depends heavily on the clarity and effectiveness of the original prompts.

Participants may interpret questions differently, provide brief responses, or discuss topics that the researcher did not anticipate.

These possibilities influenced how I approached the development of the questionnaire.

Recruiting Participants and Collecting Data

My study included 10 undergraduate public health students.

Although this sample was much smaller than what might be expected in many quantitative studies, qualitative research is not primarily concerned with achieving statistical representativeness.

Instead, the emphasis is on obtaining information that is sufficiently relevant and detailed to address the research questions.

The sample size also shaped the types of conclusions I could reasonably draw.

I could investigate patterns across participants' accounts, but I could not claim that the findings represented all undergraduate public health students.

This was an important distinction because qualitative findings are sometimes interpreted as though they establish the prevalence of particular beliefs or behaviors within a larger population.

For example, if several participants described using AI to improve their writing, that finding could establish a recurring theme within the case. It would not establish the percentage of public health students who use AI for that purpose.

I needed to remain conscious of this distinction when presenting the results.

Conducting Thematic Analysis

After collecting the responses, the next major challenge was analyzing the qualitative data.

Unlike quantitative data, which can be organized into numerical variables, qualitative responses consist of language that must be interpreted within its context.

Thematic analysis involves examining the data, identifying meaningful patterns, developing codes, and organizing related codes into broader themes.

This process required more than simply counting how often certain words appeared.

For example, two students might describe using AI to complete assignments more efficiently, but their underlying experiences could differ.

One student might use AI to clarify instructions before completing an assignment independently. Another might rely on AI to generate substantial portions of the assignment.

Although both students could describe AI as saving time, the implications for learning and academic independence might be different.

This is one of the reasons that qualitative analysis requires attention to context.

I needed to consider not only what participants said but also how their responses related to the research questions and to other responses within the dataset.

The goal was to identify patterns without eliminating meaningful differences between participants.

Identifying Themes Across Student Experiences

The analysis produced several findings that helped explain how students perceived AI in their coursework.

One recurring theme involved the perceived benefits of AI for improving writing quality and reducing the time required to complete academic tasks.

Students described advantages associated with obtaining assistance, improving written work, and completing assignments more efficiently.

However, the findings also reflected concerns about excessive reliance on AI.

Participants recognized that using AI to complete work without actively engaging with the material could interfere with learning and retention.

This distinction was important because it suggested that students did not necessarily view AI as entirely beneficial or entirely harmful.

Instead, their perceptions depended partly on how the technology was used.

Another finding involved distrust of AI detection methods. Participants expressed concerns about the reliability of attempts to identify AI-generated academic work.

The study also highlighted the perceived relevance of AI to professional environments, where students recognized that AI tools were becoming increasingly common.

Taken together, these findings suggested that students were negotiating the relationship between efficiency, learning, academic integrity, and professional preparation.

For me, one of the most interesting aspects of the analysis was the coexistence of these perspectives.

A student could recognize that AI improved productivity while also acknowledging that excessive reliance on the technology might reduce independent learning.

Qualitative methods provided an opportunity to examine these tensions rather than forcing participants' experiences into a simple positive-or-negative classification.

The Challenge of Researcher Interpretation

One of the most important methodological considerations in qualitative research is the role of the researcher.

In quantitative research, researchers make decisions about measurement, statistical procedures, and interpretation. In qualitative research, the researcher's interpretive role is particularly visible during coding and theme development.

The researcher must decide which portions of the data are meaningful, how responses relate to one another, and which patterns warrant further examination.

My professional background in undergraduate education made this especially relevant.

Having taught introductory courses and worked with students using AI, I already had experiences and perspectives that could influence how I interpreted participants' responses.

For example, I was familiar with concerns about students relying on AI to complete assignments without demonstrating their own understanding.

However, my experiences as an educator were not substitutes for the participants' accounts.

I needed to distinguish between what students actually reported and what I might have expected them to report based on my teaching experience.

This is where reflexivity becomes particularly important.

Reflexivity involves examining how a researcher's background, assumptions, and decisions may influence the research process.

Rather than assuming that the researcher can eliminate every potential source of bias, qualitative research requires transparency about the interpretive process.

Ensuring Trustworthiness in Qualitative Research

Another important lesson involved understanding how qualitative research quality is evaluated.

In quantitative research, concepts such as reliability, validity, and statistical assumptions are central to assessing methodological quality.

Qualitative research uses related but distinct approaches to establish trustworthiness.

These commonly include credibility, dependability, confirmability, and transferability.

For my dissertation, these concepts provided a useful framework for considering whether the findings were supported by the data and whether the research process was described transparently.

For example, credibility requires attention to whether the interpretations adequately represent participants' accounts.

Dependability concerns the consistency and documentation of the research process.

Confirmability emphasizes the relationship between the findings and the underlying evidence rather than the researcher's preferences.

Transferability requires sufficient contextual information for readers to evaluate whether findings may be relevant to other settings.

These considerations reinforced that qualitative research is not simply an informal collection of opinions.

It requires systematic procedures, documentation, and careful justification of analytical decisions.

Recognizing the Limitations of the Study

My study also had limitations that influenced how I interpreted the findings.

The relatively small sample and defined educational context meant that the results could not be generalized statistically to all undergraduate students.

The use of written open-ended responses also meant that I could not immediately ask participants to elaborate on unclear statements or explore unexpected issues in greater depth.

Additionally, the study relied on participants' descriptions of their own AI use.

Self-reported experiences may differ from actual behaviors, particularly when discussing a topic connected to academic integrity.

These limitations did not invalidate the study. Rather, they established boundaries around what the findings could demonstrate.

The study provided insight into how a particular group of undergraduate public health students understood and experienced AI in their education.

It did not attempt to measure the actual effects of AI on learning outcomes or establish whether AI caused improvements or declines in academic performance.

Understanding this distinction was essential when developing the discussion and recommendations.

Comparing Qualitative and Quantitative Research

Having experience with quantitative research as well, I found it useful to reflect on the differences between the two approaches.

Quantitative methods are particularly useful when researchers want to estimate relationships, compare groups, test hypotheses, or measure outcomes.

Qualitative methods are especially valuable when researchers want to understand experiences, explore decision-making, and investigate how individuals interpret a phenomenon.

Neither approach is inherently superior. Their usefulness depends on the research questions being asked.

In my Health Psychology research, quantitative methods allowed me to investigate statistical relationships among cannabis use, adverse event burden, and quality of life.

In my Public Health education dissertation, qualitative methods allowed me to explore how students understood and made decisions about AI use.

The qualitative study did not require statistical mediation models or hypothesis testing. Instead, it required close examination of participants' language and careful development of interpretations grounded in their responses.

Both approaches demanded methodological rigor, but that rigor was demonstrated in different ways.

What I Would Do Differently

Reflecting on my experience, I can identify several considerations that I would emphasize in future qualitative research.

First, I would continue to give considerable attention to the development of open-ended questions. The quality of the responses depends partly on whether participants understand what is being asked and have sufficient opportunities to explain their experiences.

Second, I would consider whether interviews might be more appropriate when a research question requires substantial follow-up or clarification.

Written questionnaires can be efficient and allow participants to respond at their own pace, but they do not offer the same opportunities for interactive exploration.

Third, I would continue to emphasize reflexivity throughout the research process, particularly when investigating topics related to my own professional experiences.

Finally, I would be careful to distinguish recurring themes from claims about how common particular experiences are within a larger population.

These considerations are especially important for doctoral students who are new to qualitative methods and may be accustomed to evaluating research primarily through statistical criteria.

Final Reflections

Conducting a qualitative dissertation for my EdD in Public Health helped me develop a deeper appreciation for the role of individual experiences in educational research.

The process required me to move beyond measuring behaviors or testing relationships and instead examine how students understood their decisions, experiences, and educational responsibilities.

It also demonstrated that qualitative research requires substantial methodological planning.

Developing appropriate questions, collecting meaningful responses, identifying themes, examining alternative interpretations, and establishing trustworthiness all require careful attention.

My findings illustrated the complexity of student perspectives on AI.

Students could appreciate the efficiency and writing assistance offered by AI while simultaneously recognizing concerns about dependence, learning retention, and academic integrity.

These perspectives would have been difficult to capture fully through questions that required students to select only predefined response options.

Ultimately, the most important lesson I took from qualitative research was that understanding an experience requires more than identifying what happened. It requires examining how individuals interpret that experience and why those interpretations matter.

For doctoral students considering a qualitative dissertation, I would encourage them to select their methodology based on the nature of their research questions rather than assumptions about which approach is easier.

Qualitative research may not require advanced statistical analysis, but it demands careful interpretation, methodological transparency, and a willingness to engage with findings that may be complex or contradictory.

My EdD dissertation provided an opportunity to develop these skills while investigating a topic with direct implications for teaching, learning, and the future use of artificial intelligence in higher education.