Qualitative and Quantitative Approaches to Research
Qualitative and Quantitative Approaches to Research
We’re going to spend the next part of this book delving into particular designs, addressing how they’re set up and how they’re evaluated. You’ll eventually be able to name them from their descriptions; this will be helpful both for your own reading of research and for planning your research proposal. For the moment, however, let’s just explore the basics of different types so you can begin to consider how the factors you’ve just read about fit together.
Grouping research designs isn’t an exact science. Sometimes people group according to the potential for causality (so you’ll sometimes see experimental, quasi-experimental, non-experimental as classifications). Others will simply group according to how the data themselves are collected. For this book, we’re going to group according to a mixture of qualities. Choosing a research design is much more than simply looking at the groupings, selecting one from the list, and applying it directly to your question. The research designs that we’ll discuss are like templates: you select one to start your project off, but then you are free (and encouraged!) to modify bits and parts as needed. In the end, your design should be appropriate to answer your particular question with the resource you have (or would have, if you were actually going to do this study).
Can I use AI for this?
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You’re the Researcher: Let AI Help—But Don’t Let It Take Over
The final way I’d like to introduce AI’s usefulness is in helping you design your study. You can apply this information to the following four chapters. Remember, when you use AI to support your research design, it isn’t cheating. The caveat is that you need to be the one making the decisions. AI is only here to assist you through the design process.
Once you’ve written your research question, it’s up to you to consider what kind of design might best help you answer it. This includes whether to use qualitative, quantitative, or mixed methods and what tools or procedures you might use to collect your data. Once you have a general direction in mind, AI can help you develop your ideas further. For example, if you’re thinking about using qualitative methods, you might ask AI, “I’m considering using interviews in my study. What are some tips for writing effective interview questions? Please don’t write questions for me. I am just looking for information to get me started?” or “What sample size is typically used in qualitative research like this?” If you’re leaning toward surveys, you could ask, “Are there any existing measurements that have proven reliable and valid that measure [your concept]?” The key is that you lead the process — you bring your design ideas to the table, and AI helps you refine them, offering examples, pointing out considerations you may not have thought of, and helping you align your method with your research question. In this way, AI becomes a support tool that enables you to grow your understanding and confidence in designing research while keeping you in complete control of your study decisions.
As you use AI to help develop your research design, it’s important to remember that you are responsible for checking the accuracy and reliability of the information it provides. If AI suggests a survey or existing scale, such as a self-esteem inventory or a resilience measure, you need to look it up through reputable sources—such as peer-reviewed articles, academic databases, or publisher websites—to confirm that the measure actually exists, is appropriate for your population, and has been validated.
In addition, academic integrity requires transparency. If AI assisted you at any point in your study design—whether in brainstorming data collection methods, outlining procedures, or generating sample questions—you should clearly state how AI was used. This models ethical scholarship and helps your instructors understand your learning process. Being thoughtful, honest, and diligent in documenting how you use AI will strengthen your work and credibility as a researcher.
I would like to give you an example of how AI might be ethically used in the design process.
If you are considering a qualitative design, you might approach AI like this:
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“I’m thinking of using interviews in my study. Can you help me compare different qualitative approaches, like focus groups or ethnography, so I can decide what fits best?”
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“I want to create a semi-structured interview guide. Here are a few questions I’ve drafted—can you help me refine them or suggest how to organize them?”
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“For my qualitative study, I’m planning to interview around 10 participants. What should I consider when choosing a sample size?”
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“I plan to use thematic analysis to code my interview data. Can you walk me through the steps or help me understand the process?”
If you are considering a quantitative design, here’s how you might engage with AI :
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“I’ve identified my independent and dependent variables—can you help me double-check if I’ve labeled them correctly and if they make sense for my research question?”
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“I’m planning to measure self-esteem in my study. I’ve heard of the Rosenberg Self-Esteem Scale—can you help me understand how it works and whether it would fit my population?”
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“I want to include questions about Instagram use in my survey. Here are a few items I’ve drafted—can you help me refine them or suggest ways to improve clarity and consistency?”
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“My study looks at the relationship between Instagram use and self-esteem scores. I’m considering a correlational design—can you help me decide which statistical test would best fit my data?”
If you are considering a mixed-methods design, here’s how you might approach AI:
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“I’m thinking of collecting both interview data and survey results. I’m not sure whether to do them at the same time or one after the other. Can you help me weigh the pros and cons of a sequential versus a convergent design?”
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“I want to use qualitative and quantitative methods in my study because I think they’ll give me different kinds of insight. Can you help clearly explain how these two approaches complement each other?”
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“I plan to compare themes from my interviews with trends in my survey data. I have a general idea of how I want to integrate my findings (state what you are thinking)—can you help me make sure I’m thinking about that process clearly and logically?”
No matter which research approach you choose—qualitative, quantitative, or mixed-methods—you are the leader of your study. AI can be a helpful support tool, but it should never be the one making your decisions. It’s your responsibility to develop ideas, questions, and drafts and then use AI to help you refine, improve, or troubleshoot your design. By taking the lead, you protect your work’s integrity and strengthen your skills as a researcher. Let AI support your thinking, not replace it. Your creativity, critical thinking, and ethical judgment will make your study meaningful and credible.
The Researcher’s Toolkit: How I Apply What I Teach
Dr. Knight- why you like qualitative
The Researcher’s Toolkit: How I Apply What I Teach
There’s something deeply human about qualitative research. It wasn’t the first research method I learned, but once I discovered it, I never looked back. For me, qualitative research is where the heart of social science lives—it’s in the stories, the voices, the lived experiences. It’s meaningful, grounded in context, and full of potential for discovery.
One of the things I love most about qualitative research is its unpredictable nature. I’ve found that some of the most powerful insights in my work have come not from my original questions, but from the participants themselves—people in interviews or focus groups bringing up ideas, concerns, or patterns I hadn’t even considered. It allows me to explore areas I might never have thought to examine without their input. That openness to discovery keeps the work fresh, relevant, and deeply engaging.
Over the years, qualitative research has allowed me to explore difficult topics, amplify underrepresented voices, and ask questions that can’t be answered with a checkbox. It’s not just a method—it’s a mindset, a way of approaching the world with curiosity, respect, and empathy.
What I want students to take from this is simple: Don’t underestimate the power of a good question and an open ear. Qualitative research may unfold in unexpected ways, but that’s often where the most meaningful learning happens. Embrace the process—you might be surprised where it takes you.
Mixed-Methods Research
There is such a thing called mixed-methods research, in which researchers combine methods from both types of research into one study, but it’s less commonly done than are studies focused in one method or the other. Combining the methods in one study an excellent idea and a worthy undertaking in some cases, but it needs to be undertaken with care. Mixed methods researchers who are actually and fully trained in both types of research are amazing but rare. Problems can arise when folks who don’t actually understand their second method very well tack it on to a study without it actually contributing something to the overall understanding of the issue at hand. Both methods should contribute something meaningful to the overall knowledge to be gained from the study. Rather than expecting one person to design and conduct both types of research, multi-disciplinary teams with researchers trained in both philosophies have the best opportunities to create some truly unique and interesting designs through combining methods in thoughtful and clever ways.
Let’s Break it Down
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Mixed Methods
* This image was created using ChatGPT; however, the concept, design direction, and creative vision were conceived by Dr. Knight
A mixed-methods approach is when a researcher uses both quantitative methods (such as surveys or statistics) and qualitative methods (like interviews or observations) to gain a more comprehensive understanding of a topic. It’s like putting two puzzle pieces together to see the whole picture.
