Mixed Methods UX Research: A Blockchain Community Case Study

October 3, 2022

Introduction: What is a Mixed Methods Research?

Mixed Methods User Research isn’t new, but it has become relatively common among UX practitioners. Opinion X highlighted a clear emerging trend in Mixed Method Research after interviewing 100+ user researchers and product managers from September to December 2020.

In the first part of the case study, we talked about the competitive analysis of crypto wallets. In this part two of the case study, I will walk you through mixed methods user research with examples and how UXBoost applies this method to study a blockchain community.

Mixed Methods UX Research_cover

Main Types of Mixed Methods Research  

Quantitative data is numerical and tells you what is happening, while qualitative data gives you insights into why it happened. We know both quantitative and qualitative methods have their pros and cons. The advantage of mixed methods research is to combine the different spectrums of the research methods, painting a thorough and complete picture of our research participants.

According to Dr. John Creswell (a research scientist focusing on the mixed method), there are three main types of mixed methods designs.

Figure 1: Types of Mixed Method Research (Adopted from OpinionX)
Figure 1: Types of Mixed Method Research (Adopted from OpinionX)

Exploratory Sequential Design: Qual → Quant

Exploratory Sequential Design focuses on qualitative research first. You would use the qualitative insights to frame the quantitative study to gain a better understanding across a wider audience. This approach works best when you are working on research questions with many unknowns and need some direction to frame the quantitative research questions or even the choices.

Explanatory Sequential Design: Quant → Qual

Explanatory Sequential Design is the opposite. You would focus on quantitative insights such as existing user base data, analytics, or survey data before diving into the qualitative research to gain insights on the context of issues identified.

Convergent Design : Quant + Qual simultaneously

The convergent design would be quantitative and qualitative research conducted simultaneously but separately. This approach is used to merge and compare two datasets for any discrepancies. The researcher would analyze the data and conclude how these two datasets support or diverge.

Case study: X coin blockchain community

This case study of X coin blockchain community demonstrates how to design and conduct mixed methods research, guiding you on how to set the scope of the research, design questionnaires, collect and analyze data, and present your research results.

Problem statement

We have identified some key questions but have limited insights into the community.

In general, we decided to use mixed methods to:

  • Frame quantitative questions and answer options.
  • Generalise the user interview insights with a larger audience.
  • Improve the validity and reliability of the research data through triangulation.

We conducted a semi-structured interview with 10 community members and followed by a large scale survey.

Steps on how to run an Exploratory Sequential Design Research

Figure 2: Timeline of an Exploratory Sequential Design research
Figure 2: Timeline of an Exploratory Sequential Design research

1. Draft the research design with clear goals and a research approach

It is crucial to have a very concise, concrete research goal. We conduct a kick-off meeting with our clients to discuss and understand what are the things they want to learn from their community.

2. Create a set of user interview questions

With the research goals in mind, we craft the interview questions. Remember, you need the key stakeholders to review the questions and give you a "thumbs up" to proceed.

Examples of user interview questions:

  • What motivates you to buy X coin?
  • What do you do with X coin?
  • Can you tell me what you understand about staking?
  • (With staking experience) What is the most important factor when choosing tokens to stake?

3. Recruit the right participants

We defined the participant's requirements and crafted a screener survey accordingly. During recruitment, we collaborated with the client's marketing team to post the recruitment post in their community channel. Once confirmed, we emailed the participants the interview slots and further instructions.

4. Conduct a pilot testing

We conducted a pilot testing a few days before kick-starting the interview. It is beneficial for us to validate the interview questions and gauge how much time is required for each session. Remember, never skip pilot testing unless you are ready to firefight issues during the actual interview, or worst, you need to recruit more participants to restart the interview.

5. Kick start the interviews.

It is best to run the interview with an observer who will take notes. It is almost impossible to pay attention to the interviewee while simultaneously taking good notes. When two people pair up for an interview, it can save time, and extra support and the ability to discuss the interview data with another person is always beneficial.

6. Analyze the data and present the first round of qualitative insights to clients.

We analyze the user interview data, group them into themes, and present general patterns to clients.

7. Use the qualitative insights to craft the large-scale survey questionnaire.

The qualitative data gives us an apparent direction on what to focus on in the large-scale survey and surfaces the important data we need to validate with a larger audience. We crafted a survey of about 20 questions focusing on demographics, motivations, staking perception, and experience.

8. Launch the survey

In collaboration with the client's marketing team, we launch the large-scale survey in the community channel. The survey was up and running for about two weeks.

9. Analyze the data

We compare both quantitative and qualitative data. The survey data further supported the general patterns found in user interviews. During the analysis, we also used statistical tests to validate if there is any significant difference between specific metrics.

10. Presentation!

To wrap up the whole project, we prepared a holistic report and presented it to the client. We only focus on key findings to prevent overloading the stakeholders, starting from the most important data to the least.


In the end, we delivered 3 main personas of X coin community that help to shape the client’s communication and define the business strategy. We also provide the whole report alongside some area of recommendations.

Figure 3: Example of Persona of X Coin Community


Mixing different research methods can bring your research to the next level. Complementing other ways improve the validity and credibility of your research data. However,  please bear in mind that things can get complicated fast due to the validity of the data. Be clear on your research intent and how you would systematically integrate and analyze different data.

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About the Author

Valentinie Wong

Valentinie is a UX researcher with a background in Cognitive Science. She has experience researching different industries, from E-government services, credit bureau, UX learning platforms, blockchain, food delivery, etc. For her, there is nothing more rewarding than seeing how user research can inspire and shape products that solve real user problems.