Data drives decisions across industries. In a virtual world, users and consumers generate immense insights. To capture time-sensitive experiences, a mixed methods approach is suitable in most cases. In this blog, we will learn about the mixed method research approach and why it is popular among researchers, CX leaders and marketers. 

What is Mixed-Methods Research Approach?

Mixed-methods research (also known as mixed methods) integrates both qualitative research and quantitative research approaches to conduct comprehensive study that covers both numerical and non-numerical aspects of a research. Mixed-methods research combines both by designing and integrating them to provide complementary insight. 

To break it down:

• Quantitative methods generate numeric, structured data: surveys, experiments, statistical analyses.
  
• Qualitative methods generate rich, descriptive data: human or AI-driven interviews, focus groups, open-ended responses, observations.
  

Why this matters

• Quantitative data can tell you how much, how often, or to what extent something happens but it struggles with the why and how.
  
• Qualitative data gives you context, meaning, nuance but on its own may lack breadth or generalisability.
  
• Mixed-methods bridges the gap: you get numbers and narrative, patterns and stories, breadth and depth.

Example of a mixed-methods research study

Example 1: A company plans to launch a new line of frozen pizzas. They want to understand consumer purchase behaviour (quantitative) and deeper motivations, perceptions, and barriers (qualitative). So they run an online survey measuring preference, purchase likelihood, price sensitivity. The company also runs in-person focus groups or in-depth interviews to probe why those preferences exist. They then combine the findings: the survey shows that 40 % of consumers prefer thin-crust, but the qualitative data reveals that one reason is the perceived “healthier” message of thin crust. That qualitative nuance helps explain the number and shapes the product messaging. 

Example 2: A health-care study used a one-year longitudinal mixed-methods design with older adults; quantitative surveys assessed loneliness and social support, while qualitative focus groups explored subjective experiences of belonging. 

An example of mixed-methods research is: a survey measuring a large sample on X, followed (or preceded) by interviews/focus-groups to elaborate on the survey findings, then integration of both to draw richer conclusions.

Other names of mixed methods research:

• Mixed-mode research or mixed-mode data collection (especially in marketing/market research contexts): this uses multiple modes e.g: WhatsApp survey for Likert scale surveys + AI-led voice interview by Maya AI powered by Merren for a single study.
  
• Multimethodology or multimethod research: General term that refers to using more than one method (but not always both qualitative and quantitative) within a study.
  
• Methodological pluralism: Emphasising multiple methods, paradigms or approaches in one research project.

The 5 Purposes of Mixed Methods Research

In the methodological literature, five core purposes (rationales) for using mixed-methods research are identified:

1. Triangulation – Using different methods to cross-validate results, i.e., to check if quantitative and qualitative findings converge or corroborate one another.
   
2. Complementarity – Using one method to elaborate, enhance, or clarify results from another method. For instance, qualitative data deepening quantitative results.
   
3. Initiation – Discovering new insights, contradictions, or questions that one method alone might miss, thus stimulating new lines of inquiry.
   
4. Development – Results from one method help inform or shape the other method (e.g., qualitative findings help develop a quantitative instrument).
   
5. Expansion – Extending the breadth and range of inquiry by using different methods for different facets of the research question (e.g., quantitative for broad scope, qualitative for deep dive).

Mixed Methods Research vs Qualitative vs Quantitative: Which Should I Use?

When neither qual nor quant alone fully answers the question, use mixed methods of research. This integrates both perspectives and improves decisions when you can plan resources for both. Avoid it when a single method suffices or when teams cannot execute integration rigorously and transparently.

How to Conduct Mixed Methods Research?

Mixed-methods research uses both traditional/standard instruments and increasingly AI-driven or digital tools to collect, analyse and integrate data. Let’s look at both categories.

Traditional instruments for mixed methods

Quantitative side:

Closed-ended questionnaires / structured surveys (online, paper) with scales, multiple choice, ranking, grid questions. Standardised tests, structured observations, numerical measures (e.g., number of clicks, frequency of events). Existing datasets (administrative records, logs) used quantitatively.

Qualitative side:

Semi-structured or unstructured interviews (in-person, telephone, online). Focus-groups, ethnographic observations, open-ended survey questions, diaries, field notes.Qualitative document analysis (e.g., transcripts, open-ended responses).

Step-by-step: how to conduct

1. Define questions: ensure the question needs breadth and depth, not just one method.
2. Choose purpose: triangulation, complementarity, initiation, development, or expansion to guide design choices.
3. Select design: convergent, explanatory sequential, or exploratory sequential; consider advanced frameworks when multi-phase or participatory contexts apply.
4. Plan sampling: align qualitative and quantitative frames (e.g., maximum-variation qualitative linked to stratified quant segments) and document connections.
5. Design instruments: ensure construct alignment across strands; pilot test for clarity and measurement quality before fielding.
6. Collect data: follow ethical procedures, timing per design, and tracking to enable integration at analysis time.
7. Analyze data: perform strand-appropriate methods, preserving traceability to integrate later without forcing convergence.
8. Integrate: build joint displays, follow threads, and reconcile convergence/divergence with design-appropriate logic.
9. Interpret and report: mirror the chosen design in structure, make integration explicit, and discuss implications and limitations.

Sampling and validity rigor

• Sampling alignment: define how qualitative sampling (e.g., extreme cases, maximum variation) maps to quantitative frames and segments to support coherent integration and legitimation.
• Quality/validity: address reliability in quant, credibility/transferability in qual, and mixed-methods legitimation (inside–outside, weakness minimization, sequential legitimation) to demonstrate robustness.
• Pitfalls and fixes: avoid token interviews after a survey; plan integration from the start, balance strand priorities, and transparently interpret conflicting findings as theory/productive insights, not errors.

Reporting structure and templates

Reports should “mirror” the selected design to aid readers and reviewers:

• Convergent: Methods detail simultaneous strands; Results present quant and qual separately, then an integrated findings section with joint displays; Discussion synthesizes alignments and divergences.
• Explanatory: Methods show quant first then qual follow-ups; Results narrate quant findings, followed by qualitative explanations, then integrated implications; Discussion bridges back to the initial hypotheses.
• Exploratory: Methods show qualitative discovery, instrument development, then quant validation; Results narrate qualitative constructs, scale development evidence, and survey results; Discussion integrates through construct-level insights.

AI-Driven Tools To Conduct Quant + Qual Research

Traditional research especially when using both qual + quant study is resource and time intensive. Insights wait for no one in a fast moving world. This is where AI-led intelligence needs to make a mark. 

• CAQDAS (Computer-Assisted Qualitative Data Analysis Software) tools such as MAXQDA and QDA Miner that support mixed-methods workflows link qualitative codes to quantitative variables for mixed datasets.
  
• Maya AI by Merren is designed for mixed methods with robust qual research tools. Generate conversational AI-interviews with intelligent follow-ups relevant to the study. Maya AI integrates smoothly with other survey tools from Merren. 
  
• Merren CX tools offer native WhatsApp surveys, Facebook surveys and chatbot/ SMS surveys. These tools do not need technical expertise and can be automated at multiple touchpoints.
  
• AI/NLP tools: For example, text-mining, sentiment analysis, thematic extraction from large open-ended responses. Maya AI can convert qualitative text into quantifiable themes or link textual data with numerical variables. This allows richer integration of large-scale qualitative data in mixed methods. 
  
• Unified analysis environments: Researchers are experimenting with computational notebooks and integrated platforms that allow both quantitative and qualitative analysis in one environment (e.g., code in Python/R for numeric analysis + qualitative coding in the same notebook).
  
• Dashboard / visualization tools: helping integrate qualitative codes and quantitative metrics like visual “joint displays” where graphs and themes appear side by side, enabling the researcher to see the convergence or divergence of findings across methods.

Structure Example of a Mixed-Methods Research Project

To help you structure a mixed-methods research study, here is a step-by-step outline:

1. Define research question(s): ensure the question merits mixed methods (breadth + depth).
   
2. Articulate purpose: is your goal triangulation, complementarity, initiation, development or expansion? (see the five purposes).
   
3. Select design : common mixed-methods designs include:
   
   
   • Convergent Parallel (collect qual + quant at same time, analyse separately, then merge)
     
   • Explanatory Sequential (quantitative first → qualitative follow up)
     
   • Exploratory Sequential (qualitative first → quantitative follow up)
     
   • Embedded (one type of data supports the other within a larger design)
     
     
4. Design instruments: for quantitative and qualitative data; ensure alignment and integration pathways.
   
5. Data collection: follow sampling, administration, ethical procedures for both strands.
   
6. Data analysis: analyse quantitative and qualitative data (separately or in sequence as per design).
   
7. Integration of results: merge, connect, or embed results; use joint displays, narrative linking, transformation of one form to another.
   
8. Interpretation and reporting: draw inferences from integrated data; highlight how qualitative data explains quantitative patterns, or vice versa. Address discrepancies, divergences.
   
9. Reflection & limitations: Mixed-methods research is powerful but complex: resource intensive, demands multiple competencies, risks conflicting findings.

Maya AI: Overcome Mixed-Method Research Challenges

Maya AI is an intelligent customer research tool that is AI-driven for maximum impact for qual research. Maya AI is every researcher’s AI-powered companion for faster insights at 8x lesser cost:

No burning budgets with traditional approach

Maya AI is an AI interview that works in a few clicks. Generate discussion questionnaires and customize it according to your industry. Reach maximum real customers at moderate cost.

No human interference, no bias in data

Maya AI collects data objectively without emotional drivers. However, Maya collects emotional and sentiment driven metrics with intelligent follow-up questions. This conversational approach encourages people to share genuine responses. 

Designed to minimize complexity that comes with research 

Maya AI’s interface is systematically designed by veteran market researchers who understand the process of conducting research from scratch. All you do is generate a discussion guide questionnaire with a prompt and customize your AI-interviewer based on language and other preferences. Maya captures metrics that human researchers may miss.

Get data-driven insights in days, not weeks

Conduct 30 interviews before lunch and 30 more before the day is over. Automation, unified dashboard and reporting analysis simplifies complex tasks. Collect responses and compile complete data anywhere from a few hours to a few days as per the extent of research.

Implications for Practice (especially in CX, Marketing, Business)

For professionals working in customer experience (CX), marketing research or business intelligence, the mixed-methods approach offers tangible benefits:

• When you run a large-scale quantitative survey (e.g., customer satisfaction scores) and conduct in-depth interviews/focus-groups to understand the why behind the numbers, you create actionable insights: the numbers highlight problem areas, the stories explain them, and you can tailor interventions accordingly.
  
• For market research launching a product: quantitative data shows market size, segments, purchase intent and qualitative data reveals unmet needs, emotional triggers, messaging language. Mixed methods build both a quantitative business case and an emotional narrative for positioning.
  
• For CX measurement: track Net Promoter Score (NPS) or CSAT quantitatively, and conduct open-ended feedback analysis or voice-of-customer interviews to turn numbers into meaning.
  
• Mixed-methods tools (like CAQDAS or mixed-mode survey platforms) help streamline workflows especially important when you’re analysing both scale (numbers) and narrative (text).
  
• In the context of digital and AI-driven research, you might use automated sentiment analysis or text-mining of open-ended responses (qual data) alongside click-stream or behavioural analytics (quant data). Integrating these within a mixed methods framework can yield sophisticated insights.

FAQs on Mixed Methods Research

Is mixed methods better than single-methods?

Neither is universally better. Mixed methods are superior when the research requires both measurement, explanation and when integration can be executed rigorously and transparently.​

What are the main designs?

Convergent, explanatory sequential and exploratory sequential with advanced frameworks like multistage, intervention, case study, and participatory for complex programs and health contexts.​

What about sample sizes?

Sample size is driven by strand needs: statistical power and representativeness for quant, and saturation/variation for qual; plan linkage so integrated inferences are credible and balanced.​ Calculate sample size here. 

How do you handle conflicting findings?

Treat divergence as insight: revisit assumptions, refine segmentation, or adjust theory; report conflicts transparently and explain how the integration informed design or decision changes.