Mixed methods research designs for beginners

What is mixed methods research, should you use it in your study? If so, how should you use it, what might it look like?

Would you collect qualitative and quantitative data at the same time? One before the other? How would you analyse them?

Yeah, there’s a lot of questions there aren’t there?! In this blogpost I’m going to start to unpick some of this stuff so you can have a better understanding of mixed methods and where it might be helpful in your research.

In recent years, mixed methods research has become increasingly popular, and it’s being used in more disciplines than ever before, which is great! But, PhD students ask me a lot of questions about it - there’s a lot of confusion out there - so lets start clearing that up today.

(1) What is mixed methods research?

Mixed methods research is exactly what it sounds like - it’s a combination of two research approaches: quantitative research and qualitative research.

But why combine these two? Well, let’s start with the basics.

Quantitative research is all about numbers. You gather data that can be measured, like survey responses, test scores, or statistical data. You then analyse that data numerically to uncover patterns, trends, and relationships.

On the other hand, qualitative research is about understanding people’s experiences, perspectives, and emotions. It uses things like interviews, focus groups, and open-ended survey questions to capture detailed, descriptive information that can’t be easily quantified.

But what if you could use both to answer your research questions? That’s the magic of mixed methods! It allows you to take the best of both worlds - using numbers to quantify your findings and rich, detailed data to explore deeper meanings and insights.

(2)Where can mixed methods research be valuable?

One of the major advantages of mixed methods is that it helps you answer more complex research questions. Sometimes, numbers alone can’t explain the full story, and qualitative data alone can be too subjective.

For example, if you’re studying student satisfaction with online learning, quantitative data from a survey might show that most students are satisfied. But the ‘Why?’ behind that satisfaction is where qualitative data comes in. You could interview students to understand what specifically makes them happy or frustrated with online learning.

In other words, mixed methods research helps you build a fuller picture by combining the breadth of quantitative data with the depth of qualitative insights.

(3) Types of mixed methods research designs

Now let’s talk about how mixed methods research actually works.

There are several designs you can use to structure your study, and each one depends on how you want to integrate your quantitative and qualitative data.

Convergent Design

One of the most common designs is the Convergent Design. In this design, you collect both quantitative and qualitative data at the same time, analyse them separately, and then compare, contrast, validate or merge the results. It’s like bringing together two pieces of a puzzle to see how they fit.

For example, you might gather quantitative data on people’s use of a fitness app and also do interviews to understand why they use it in the way they do, then compare the usage numbers with the qualitative data from the interviews to see the bigger picture.

Explanatory Sequential Design

Another design is the Explanatory Sequential Design, where you start with quantitative data collection and analysis, then use qualitative data to explain or expand on the findings.

For example, you might survey students about their online learning experience and then follow up with interviews to understand why they feel that way.

Exploratory Sequential Design

Then there’s the Exploratory Sequential Design, where you start with qualitative data to explore a phenomenon, then use quantitative data to test or measure the patterns that emerged from the qualitative phase.

For example, you might interview people to understand why they prefer certain brands of coffee, then create a survey to measure how common those preferences are among a larger group of people.

The importance of integration

Now let’s talk about integration - the real heart of mixed methods research. This is the most defining feature of mixed methods and what makes it so powerful. Integration is all about bringing together your quantitative and qualitative data to create a cohesive understanding of your research question.

But you’re not just sticking them both in a bowl and stirring - they need to be interwoven.

Think of integration as the bridge that connects your two sets of data. Simply collecting both types of data isn’t enough. You need to bring them together in a meaningful way. This allows you to see the relationship between the numbers and the narratives, giving you a fuller, richer understanding of your topic.

Lets walk through integration within those three different types of mixed methods.

Integration in the Convergent Design

First, let’s talk about the Convergent Design. In this design, integration happens by merging the results from your quantitative and qualitative data to make a comparison and get a more complete understanding.

To recap, you gather both sets of data—like survey responses (quantitative) and interviews (qualitative)—at the same time.

Once you’ve collected the data, you compare and combine the results to see how they align or contrast with each other.

This integration gives you a richer understanding of the topic than you’d get from just one type of data.

For example, imagine you’re studying customer satisfaction at an organic food store. You could distribute a survey (quantitative) asking customers to rate their satisfaction on a scale of 1 to 10, and you might find that most customers give high satisfaction ratings.

However, you could also do qualitative interviews at the same time. You might learn, for example, that while customers are generally happy with the product quality, they feel the store is too expensive.

In other words, you’re merging the numbers with the stories. The survey tells you what people think, and the interviews explain why they think that way, giving you a more complete picture of customer satisfaction.

Integration in the Explanatory Sequential Design

Next, let’s look at the Explanatory Sequential Design. In this design, integration works a little differently. Here, you start with quantitative data collection, analyse it, and then use that analysis to guide the next step—qualitative data collection.

To put it simply, you collect and analyse the quantitative data first.

Then, based on what the numbers tell you, you plan your qualitative phase. This phase is designed to help you dive deeper into the quantitative results, probing specific areas where you need more explanation or understanding.

The numbers inform what you do in the qualitative phase – the qualitative phase is shaped by the quantitative phase.

Let’s say you’re studying customer satisfaction at the organic food store again. You might first collect survey data from customers, asking them to rate their satisfaction with aspects like product variety, prices, and service.

After analysing the survey data, you find that while most customers are happy with the product selection, many rate the prices poorly.

So, for the qualitative phase, you might decide to conduct interviews with some of the dissatisfied customers to explore the reasons behind their dissatisfaction—perhaps customers feel the prices are too high compared to other stores, but they value the organic selection.

In other words, the survey results guide the follow-up interviews, helping you focus on the specific issues that need further exploration.

Integration in the Exploratory Sequential Design

Finally, let’s look at how integration works in the Exploratory Sequential Design. In this design, integration happens in the opposite order. Here, you start with qualitative data, analyse it, and then use the results to inform the development of a new solution, intervention, or measure, which you’ll then test quantitatively.

So, you begin by gathering qualitative data to explore a topic. After analysing that data, you use the insights you’ve gathered to create something new—like an intervention, a tool, or a survey measure.

Then, you use quantitative methods to test or validate your new tool or solution.

Let’s stick with the example of the organic food store. You might start by conducting interviews with customers to explore their experiences with shopping at the store. Through these interviews, you might discover that many customers would be more willing to shop at the store regularly if there was a loyalty programme or discounts for bulk purchases.

Based on this feedback, you create a new loyalty programme. Afterward, you design a survey to test whether customers’ satisfaction and frequency of visits improve with the introduction of the programme

In other words, you begin by gathering feedback, use that to create something new (like a loyalty programme), and then test it with a quantitative survey to measure its impact.

Integration summary

So the way integration works really depends on the design you choose.

In the Convergent Design, you merge both sets of data to compare and create a full picture.

In the Explanatory Sequential Design, you use the quantitative results to guide the qualitative phase, diving deeper into the findings.

And in the Exploratory Sequential Design, you start with qualitative data to build something new and then use quantitative methods to test it.

So, integration isn’t just about gathering both types of data—it’s about using one to enrich or inform the other, based on the design you choose.

I hope you found this dive into mixed methods helpful!

Be sure to check out my other blogposts on all things methodology here!