Understanding how researchers collect and interpret data is essential for designing purposeful studies. This article outlines the foundations of qualitative and quantitative approaches, with insights from Dr. Nicholas Markette, assistant dean and EdD program chair for the College of Doctoral Studies.

The key difference between quantitative and qualitative research is in their data collection and interpretation. Quantitative research collects numerical data for statistical relationships, while qualitative research gathers descriptive data through interviews and observations to understand how and why people think and act in specific contexts.

As Dr. Markette puts it succinctly: “Quantitative research - the data comes in numbers. Qualitative research - the data primarily comes in the form of words.”  The two approaches differ in scope and purpose. Quantitative designs aim for broad generalization across larger samples to answer questions about what is happening and to what extent. For example, Dr. Markette explains, “If I want to know, does teacher self‑efficacy predict teacher burnout, I’m going to run surveys across 100 to 300 teachers… and I will know this broad idea of these 300 teachers.”

Qualitative designs seek to deeply capture lived experiences and illuminate meaning, process and context. Using the same example, Dr. Markette notes, “If I want to investigate how people experience self‑efficacy and burnout in a school, I can go and interview twenty of those teachers and go deeply, deeply… into that topic and pull those words from them and then analyze those to understand how and why.”  

Quantitative methods provide broad insights across many participants, while qualitative methods explain the how and why within specific cases. Both approaches involve rigorous analysis but use different raw materials; quantitative studies apply statistics to numerical data. 

Choosing between qualitative and quantitative depends on aligning the research question, feasibility and the researcher’s readiness with the most suitable method. As Dr. Markette states, “The scholar is like a carpenter, and the carpenter goes to his or her toolbox and selects the tool that best fits the job.” That’s the technical, scholarly answer.

First, nuance comes into play based on your question type. Qualitative inquiries focus on how people describe or experience a phenomenon, while quantitative inquiries explore relationships, quantities or differences between variables. Dr. Markette offers concrete contrasts:

Example 1 

“How do online doctoral students describe their experience working full-time, raising a family and completing their dissertation?” This is qualitative (because you’re asking how they describe it). 

“To what extent do hours spent working predict time to completion for a dissertation?” This is quantitative (you’re comparing numbers).

Example 2

“Is there a difference in graduation rates between generations (Baby Boomers, Gen X, Gen Z) in the doctoral program?” This would be quantitative (group comparisons by rates). 

“How do different generations describe their experience in an online doctoral program?” This would be a qualitative multi‑case study (multiple groups, experience described in words).

Second, weigh the feasibility and your skill set. Dr. Markette is candid that personal comfort can influence the choice more than it should: “A lot of people make an emotional decision from quant, ‘I’m not a numbers person.’” He even has a lesson titled “Don’t fear the quant monster.” His guidance is to separate fear from a fundamental skills gap: “Is this a fear or is this a real skill issue? Because we have so many tools now that can get you through the skill issue.” In practice, if your question is best answered with statistics and you have support, consider the quantitative path, even if it seems daunting.

Third, be realistic about time and workflow. Dr. Markette’s rule of thumb: “Qual is easy to get into but hard to get out of. Quant is hard to get into because you have to know what you’re doing. But it’s easy to get out of.” In other words, qualitative studies can be simpler to launch but require extensive coding and thematic analysis. Imagine “taking 200 pages of transcripts and pulling the meaning out.” Quantitative studies often demand more front‑loaded design, but once the data are properly collected, “the numbers are the numbers.”

Finally, remember that methods can also stretch and strengthen you. Dr. Markette shares that although he comes from a numbers background, he completed a qualitative study; his wife, from a liberal arts background, completed a quantitative study. The outcome? “It was exactly what I needed to be better at this part that I tried to ignore… it forced her to develop a new skill set… we were both served.” 

Data Collection for Qualitative Research 

Qualitative research focuses on understanding how individuals interpret their experiences within social, cultural and historical contexts rather than measuring variables numerically. It prioritizes meaning, perspective and process, drawing insights from sources such as:

• Interviews and focus groups
• Documents and archival materials
• Personal narratives or reflective writing
• Cultural or historical records
• Direct observation

Using open‑ended questions and flexible follow‑ups, researchers collect rich, original data that allows participant insights to emerge organically. This approach enables scholars to identify themes, patterns and contrasts, offering a nuanced understanding of lived experience within practical settings.

Data Collection for Quantitative Research 

Quantitative studies employ structured data collection to produce numerical data for statistical analysis, measuring variables, testing hypotheses and exploring relationships in larger populations. Standard quantitative data collection methods include:

• Experiments
• Questionnaires
• Surveys
• Existing databases and reports

Quantitative methods use closed‑ended survey questions to generate countable data, such as “yes” or “no” responses and numerical ratings. This approach emphasizes consistency and replicability through standardized data collection, allowing researchers to analyze patterns across large samples and draw generalizable conclusions about variable relationships. Unlike qualitative research, which focuses on depth, quantitative research prioritizes breadth, precision and measurement.

Qualitative research focuses on understanding experience and meaning through detailed narratives, making it suitable for exploring “why” questions. In contrast, quantitative research analyzes numerical data to identify patterns, trends and connections, answering “what,” “how many” and “to what extent,” which helps confirm or challenge hypotheses and informs decision-making.

Qualitative and quantitative research methods each have strengths and challenges. Recognizing these differences aids researchers in designing studies, collecting data and interpreting results.

Benefits of Qualitative Research

Qualitative research offers notable advantages, particularly its flexibility, allowing researchers to adapt their focus as new insights arise instead of adhering to a rigid structure. 

Qualitative methods also support:

• Creativity in data collection and analysis, enabling researchers to explore complex or unexpected themes
• Multiple interpretations of data, which can reveal diverse perspectives and deeper meaning
• An evolving research scope, allowing questions, concepts or areas of interest to shift as understanding grows

This adaptability makes qualitative research particularly useful for exploratory studies and for examining phenomena that are not yet well understood.

Limitations of Qualitative Research

Qualitative research offers flexibility and depth, but also poses challenges in data collection and researcher technique. As Dr. Nicholas Markette notes, “Collecting it.” That’s where many students struggle. Much qualitative data comes from interviews, open‑ended questionnaires and focus groups, and “creating those sources of data is a skill set.” Being personable isn’t enough. “There is a misnomer, because I’m good with people, I’m going to be good at interviewing,” he explains. Effective interviewing involves listening for cues, asking layered follow‑ups and encouraging participants to share detailed information for rich, analyzable data.

Dr. Markette shares a typical pattern he sees among doctoral students: early interviews are short, “their first interview is 20 minutes, next one’s 25 minutes,” but by the time they’ve practiced, “their 15th interview is an hour and five minutes.” The difference is technique. For example, if a participant mentions juggling work and family responsibilities, a strong interviewer recognizes the cue and probes: “How do you manage that stress?” This persistent, thoughtful digging is what turns surface‑level responses into substantive qualitative evidence.

Beyond interviewing, students often underestimate the craft involved in writing effective open‑ended questionnaires and conducting focus groups. As Dr. Markette emphasizes, “Conducting focus groups is a skill, and so that’s probably the biggest challenge associated with qualitative data collection.” Poorly phrased prompts and weak facilitation can limit responses and participation, leading to biased discussions and deviations from the research questions.

These collection challenges compound other well‑known limitations of qualitative research:

• Time‑intensive analysis: Transcribing, coding and synthesizing large volumes of text (interviews, focus groups, documents) demands significant time and methodological rigor.
• Subjectivity and consistency: Interpretation relies on the researcher’s judgment; without clear coding frameworks and audit trails, findings may be less replicable.
• Context specificity: Results are often bound to specific settings and participants, limiting generalizability to larger populations.

Recognizing these challenges, programs increasingly build practice and feedback into coursework. As Dr. Markette notes, “We’ve put it into our program… we have practice interviews in the coursework now,” so students can develop these skills before collecting dissertation data. 

Benefits of Quantitative Research

Quantitative research is advantageous for measuring variables, testing hypotheses and identifying patterns in large populations. Its main strength lies in precision, as it uses numerical data for objective analysis of trends, allowing conclusions based on statistical evidence rather than personal interpretation. 

Key benefits include:

• Objectivity and clarity: Numerical results simplify communication and comparison across studies. Charts, tables and models succinctly summarize outcomes.
• Generalizability: Larger, representative samples enhance the applicability of findings to broader populations.
• Consistency and replicability: Standardized methods enable replication of studies and verification of results.
• Efficiency in data analysis: Statistical software expedites the processing and analysis of large datasets.
• Testing relationships and predictions: Quantitative methods help explore correlations, differences between groups and potential cause‑and‑effect relationships.

Limitations of Quantitative Research

Quantitative research provides objectivity and precision but has limitations, such as restricted response formats. Closed‑ended questions aid analysis but may overlook nuances and introduce measurement error if they fail to capture the intended construct.

From a feasibility standpoint, sample access is often the most significant hurdle. As Dr. Nicholas Markette explains, “Quantitative data collection. The biggest challenge is access. To be statistically valid and reliable, you need to have a large enough sample.” Power analysis may indicate the need for dozens — or hundreds — of respondents, but “getting 75 people to respond to a survey” can be difficult, especially outside of captive populations. Response quality also suffers as surveys grow longer: “We know as we start going over 15 minutes to complete it, our attrition rate drops.” Even among those who respond, “you can get responses back that are not all valid because they skipped a section or things of that nature.”

New data‑quality threats compound these issues. Dr. Markette notes the rise of third‑party survey services and automated completion: “There are people out there who have AI engines, and they’re just completing surveys for five or 10 bucks a pop, and we don’t even know if it’s real.” To mitigate this, researchers increasingly embed attention and validity checks — for example, items like “green crayons are everyone’s favorite” — so that “cockamamie” answers flag likely bots or inauthentic respondents.

Additional limitations to consider include:

• Construct oversimplification: Complex phenomena risk distortion when reduced to fixed response options or single indicators.
• Instrument bias: Poorly validated scales and unmet statistical assumptions can cause misleading findings.
• Limited contextual insight: Statistical relationships indicate connections between variables but don’t clarify underlying reasons, often requiring qualitative follow-up.
• Nonresponse bias: Systematic differences between respondents and nonrespondents can skew prevalence estimates and relationships.

Qualitative data analysis is non‑numerical and requires careful interpretation rather than statistical testing. Researchers must ensure rigor and transparency to reflect participants’ perspectives accurately. The focus is on identifying meaning, patterns and relationships within words or observations. The process is iterative, with researchers frequently refining themes and interpretations based on the data.

Several well‑established methods are commonly used to analyze qualitative data, including:

• Content analysis: Organizes and categorizes recurring words and themes to identify patterns and relationships in data
• Discourse analysis: Examines language use within social, cultural or institutional contexts, focusing on power dynamics and norms
• Thematic analysis: Identifies and interprets recurring themes to uncover underlying meanings in responses

Analyzing Quantitative Data

Quantitative data analysis involves standardized numerical measurements and statistical procedures. Researchers clean, use descriptive statistics to summarize patterns and employ inferential statistics to generalize findings. Doctoral researchers create visual summaries to check distributions and identify outliers, ensuring that statistical tests align with their research questions and designs.

Here are a few quantitative analysis tools: 

Study design Guidance
According to Dr. Markette, “Laerd will help you go through a series of questions. And those questions will help recommend the data analysis process that you should go through. So, it may say you should go through a regression analysis, [and] these are the tests of assumptions you should run, and it’s citable.”

Analysis Software
“The old mainstay for data analysis for years and years has been SPSS, but Intellectus has since come out and is far more polished… it actually gives you an explanation of what the numbers mean… it helps students learn as they’re going along, “says Dr. Markette.

Productivity and Post-Dissertation Workflows
Markette notes that, “With Copilot attached to Microsoft Excel, I’m really curious to see if that doesn’t become the new frontrunner… In iterations after the dissertation, Excel may become the main tool because we don’t care about your work; we care about the results.”

Sample Size Planning
“If you want to establish how large your sample needs to be, there is a tool called G*Power. You’ll answer some questions, and then it calculates what your anticipated sample size should be. And we may advise that you go 15% higher, just in case your data comes in nonparametrically,” says Markette.

Start with clean, well organized data and clear visuals; choose analytic tests that match your questions and variables; run and report assumption checks; and use tools that support both rigor and clarity. Finally, remember the scholarly expectation around transparency, “When you’re in junior high, you have to show your work so we understand your thinking… After the dissertation, in some contexts, we care about the results. But during your dissertation, document your steps: tests of assumptions, justifications for tests and how you handled anomalies.”

At Grand Canyon University, the College of Doctoral Studies is built on a simple, human-centered premise: doctoral students do well when they have both a strong community and expert, hands-on guidance. As Dr. Markette explains, “We have poured ourselves into creating community for these students.” That commitment shows up in two complementary forms of support:

Community That Reduces Isolation
Doctoral work can feel isolating, but it shouldn’t. According to Dr. Markette, “They need the community support where they support one another and don’t feel like they’re the only person experiencing something.”

• DC Network and Weekly DC Cohort: Every Tuesday night, GCU hosts a student-run DC cohort on Zoom, sponsored by Dr. Renee Winter, a full-time professor. This gathering is intentionally designed to help doctoral learners “connect virtually as humans.”
• A Christ-Centered Ethos: This communal approach reflects GCU’s Christian worldview, “it’s really consistent with our Christian tradition…to bring Christ into the equation by being deeply human.” Whether serving 4,000 graduates and 4,000 doctoral students, the goal is clear: “make everyone not feel… isolated and bring them all into community.”

Technical Guidance and Expert Mentorship
The second pillar is targeted, technical support that helps learners execute high-quality research with confidence. As Dr. Markette puts it, students “need the actual technical support to be able to do something.” That’s where GCU’s faculty chairs, methodologists and content experts come in. Mentorship at GCU is holistic and hands-on.

This balance is what distinguishes GCU’s doctoral mentorship culture: “We’ve attracted chairs and methodologists and experts that are some of the best in the business with their expertise, but they bring this human element to it also.” In short, “mentoring is really that, coming in fully human and addressing the doctoral student as a full human, not somebody that just needs expertise dumped into their brain.”

As you reflect on the differences between quantitative and qualitative research, consider how each approach aligns with your academic goals, professional interests and the kinds of questions you feel called to explore. Doctoral study is not just about choosing a methodology; it’s about developing the confidence and competence to apply the right tools to life’s challenges. We invite you to learn more about our doctoral programs and how our faculty mentors support students at every stage of the research journey, equipping them to design rigorous studies, contribute thoughtfully to their fields and grow as scholar practitioners.