This article shows how qualitative research isn’t limited to interviews, observations, or documents. It explores three big expansions:

1. Mixed Methods
2. Action Research
3. Arts-Based Research

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1. 🎯 Mixed Methods Research

What it is:

Mixed Methods Research is a type of research that combines quantitative methods (numbers, statistics, surveys, experiments) with qualitative methods (words, interviews, observations, stories) in the same study. It is basically the idea is that numbers tell you what is happening, while stories help you understand why and how it’s happening.

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✅ Example

Suppose you want to study student stress in graduate school:

• Quantitative part: You give a survey to 200 students and find that 70% report high stress.
• Qualitative part: You interview 10 students and learn that the stress mainly comes from financial struggles and lack of sleep. These Together, gives you a complete picture: not just the percentage of stressed students (numbers) but also the reasons behind it (stories).

1. 🏥 Healthcare Example – Patient Satisfaction

• Quantitative part: A hospital gives out surveys to 500 patients. Results show 85% are “satisfied” with their care.
• Qualitative part: The hospital also interviews 20 patients. Patients explain that while doctors are great, waiting times are frustrating.
• Together: Numbers show overall satisfaction, but stories reveal why some patients are unhappy.

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2. 📱 Technology Example – Social Media Use

• Quantitative part: Researchers track screen time data and see that teens spend an average of 4 hours a day on TikTok.
• Qualitative part: Focus groups with teens reveal they use TikTok to connect with friends, relieve stress, and follow trends.
• Together: Numbers give the extent of use, while stories explain the purpose and meaning.

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3. 🏫 Education Example – New Teaching Method

• Quantitative part: A teacher tests a new math teaching method and finds that test scores improve by 15% compared to last year.
• Qualitative part: Classroom observations and student interviews show students enjoy the method more because it’s interactive.
• Together: Numbers show the method works academically, while stories show it engages students emotionally.

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4. 🛒 Business Example – Customer Loyalty

• Quantitative part: A supermarket runs a survey and finds that 60% of customers use their loyalty card regularly.
• Qualitative part: Interviews reveal that customers like discounts but find the app confusing.
• Together: Numbers tell how many people use the card, but stories tell why usage isn’t higher.

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👉 In short: mixed methods = surveys/experiments (numbers) + interviews/observations (stories).
It’s like watching a movie in both black & white (quant only) and then in color (qualitative adds depth).

Why do it?

• Numbers tell you what is happening (e.g., 70% of students are stressed).
• Stories tell you why/how it’s happening (e.g., students describe workload and lack of sleep).

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Types of Mixed Methods

🔢➡️📖 Sequential Explanatory Design

📌 What it is

• A two-phase design where you:
  1. Start with quantitative data (numbers, surveys, experiments).
  2. Follow up with qualitative data (interviews, focus groups, observations) to explain or interpret the numbers.

👉 The idea: Numbers tell you what is happening. Stories explain why it’s happening.

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🛠 How it Works (Step by Step)

1. Phase 1: Quantitative Research
   • Collect numeric data.
   • Example: A survey of 500 college students about stress levels → 65% report “high stress.”
2. Analyze the Numbers
   • Look for trends, patterns, or surprising results.
   • Example: Stress levels are highest among first-year students, and women report more stress than men.
3. Phase 2: Qualitative Research
   • Based on your quant findings, design interviews or focus groups to dig deeper.
   • Example: Interview 20 first-year women who reported high stress.
4. Interpret Qualitative Results
   • Identify the reasons behind the numbers.
   • Example: Students say stress comes from:
     • Financial pressure 💵
     • Lack of sleep 😴
     • Fear of failing classes 📚
5. Integrate Findings
   • Merge both phases to create a complete explanation.
   • Example: Survey tells you stress is widespread (65%). Interviews reveal why stress happens.
   • Final insight: Stress isn’t just academic → it’s also economic and personal.

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🎯 When to Use

• When numbers alone are incomplete or confusing.
• When you want to explain surprising results.
• When a funder, policymaker, or school leader says: “Okay, but why do the numbers look like that?”

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🧾 Practical Examples

🏫 Education Example

• Quantitative: 1,000 students take a survey → 40% fail math.
• Qualitative: Interviews with 30 failing students reveal poor study habits and fear of asking for help.
• Integration: Numbers show the scope of failure. Stories explain the cause.

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🏥 Healthcare Example

• Quantitative: Survey shows 70% of patients use telehealth.
• Qualitative: Focus groups reveal why: convenience for some, but frustration with technology for others.
• Integration: Telehealth is popular overall, but design needs improvement for older patients.

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🛍️ Business Example

• Quantitative: Sales data shows online purchases doubled in the last year.
• Qualitative: Customer interviews explain that free shipping and convenience were the main reasons.
• Integration: Numbers prove the growth, stories explain the motivation.

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⚖️ Pros & Cons

Pros:
✅ Straightforward and logical (easy to design).
✅ Quant findings guide the follow-up interviews.
✅ Provides strong explanations for statistical results.

Cons:
❌ Takes more time (two phases).
❌ You must be skilled in both quantitative AND qualitative methods.
❌ If qualitative results don’t match quantitative results, integration can be tricky.

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👉 Beginner takeaway:
Sequential Explanatory = start with numbers → then collect stories to explain them.
Think of it like:

Stories = the article beneath the headline (why students are stressed).

Numbers = the headlines (“65% of students stressed”).

🔍 Sequential Exploratory Mixed Methods Design

📌 What it is

• You start with qualitative research (stories, interviews, observations) to explore something new or not well understood.
• Then you design a quantitative study (surveys, experiments, stats) to test, measure, or generalize what you found in the qualitative phase.

Think of it as:
👉 Explore first, then confirm.

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🛠 How it Works (Step by Step)

1. Explore (Qualitative Phase)
   • Ask open-ended questions to a small group.
   • Goal: Generate new insights, themes, or theories.
2. Identify Key Themes/Patterns
   • From interviews or focus groups, find recurring ideas.
   • Example: Business owners keep mentioning automation, marketing, and data analysis as ways they use AI.
3. Develop Quantitative Measures
   • Use those themes to design a survey or scale.
   • Example: Add survey questions like:
     • “Do you use AI for automation?”
     • “Do you use AI for marketing?”
     • “Do you use AI for data analysis?”
4. Test (Quantitative Phase)
   • Send the survey to a larger sample.
   • Goal: See how common those themes are and whether they hold up across a wider population.
5. Combine Results
   • Qual tells you what exists.
   • Quant tells you how widespread it is.
   • Together, you get both depth and generalizability.

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🎯 When to Use

• When little research exists on a topic.
• When you need to build a new framework or theory.
• When you want to test whether your initial ideas apply more broadly.

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🧾 Examples

🏫 Education

• Qualitative phase: Interview 15 teachers about online teaching. Themes: student engagement, tech problems, flexible schedules.
• Quantitative phase: Survey 500 teachers.
  • 70% report engagement as the top challenge.
  • 60% mention tech problems.
• Insight: Engagement is the biggest, most widespread issue.

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🏥 Healthcare

• Qualitative phase: Interview 10 patients about telehealth. Themes: convenience, privacy concerns, lack of physical touch.
• Quantitative phase: Survey 1,000 patients.
  • 80% say telehealth is convenient.
  • 55% worry about privacy.
• Insight: Convenience is widely valued, but privacy is a common barrier.

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🛒 Business

• Qualitative phase: Interview 12 shoppers about why they buy organic products. Themes: health benefits, environmental concerns, taste.
• Quantitative phase: Survey 600 shoppers.
  • 65% prioritize health, 30% environment, 5% taste.
• Insight: Health is the main driver — much more than environment or taste.

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⚖️ Pros & Cons

Pros

• Helps create new knowledge where little exists.
• Builds better surveys/scales grounded in real experiences.
• Allows theory building + testing in one design.

Cons

• Takes more time (two phases back-to-back).
• Need skills in both qualitative and quantitative methods.
• Harder to integrate results if themes don’t align with numbers.

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👉 Beginner takeaway:
Sequential Exploratory = start with stories to discover what’s going on → then use numbers to see how common it is.

Think of it like:

• Qualitative = detective work (find clues).
• Quantitative = census (see how widespread those clues are)

🔄 Concurrent (Parallel) Mixed Methods Design

📌 Definition

• You collect quantitative data (numbers) and qualitative data (stories, interviews, observations) at the same time.
• Then you compare, merge, or interpret the two sets of data together.

This way, you get two perspectives on the same problem at once.

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🛠 How it Works (Step by Step)

1. Define the Research Question
   • Example: “What are people’s exercise habits, and what factors influence them?”
2. Collect Quantitative Data
   • Give a survey to 300 people.
   • Question: “How many times per week do you exercise?”
   • Result: 25% exercise 3+ times a week, 75% exercise less.
3. Collect Qualitative Data (at the same time)
   • Run 4 focus groups with 6–8 participants each.
   • Ask open questions like:
     • “What makes it hard for you to exercise regularly?”
     • “What motivates you when you do exercise?”
   • People respond: “Gym memberships are expensive,” “I work long hours,” “My neighborhood isn’t safe for walking.”
4. Analyze Each Dataset Separately
   • Quant side: Run stats to find patterns (e.g., younger adults exercise more than older adults).
   • Qual side: Identify themes (e.g., barriers = cost, time, safety).
5. Merge Findings
   • Numbers show the extent of the issue.
   • Stories explain the reasons behind the numbers.
   • Together: Only 25% exercise regularly, and the main reasons are cost, time, and safety concerns.

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🎯 Why Use Concurrent Design?

• Efficiency → Saves time since both types of data are collected simultaneously.
• Triangulation → You can cross-check findings from numbers and stories.
• Comprehensiveness → Gives both breadth (survey) and depth (focus groups).

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🏫 Another Example – Education

• Research question: “How effective is online learning for high school students?”
• Quantitative: Survey 200 students → 70% say online learning helps them keep up with classes.
• Qualitative: At the same time, interview 20 students → they explain that while it helps, they feel isolated and miss in-person discussions.
• Conclusion: Online learning works academically but has social drawbacks.

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🛍️ Another Example – Business

• Research question: “Why do customers shop online instead of in-store?”
• Quantitative: Track 1,000 purchases → 80% happen online.
• Qualitative: Conduct 15 interviews with frequent online shoppers → they say convenience and free shipping matter most.
• Together: You know how many shop online and why they do it.

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👉 Beginner takeaway: Concurrent design = do both at once. Think of it like taking a selfie with two cameras — one shows the wide view (numbers) and the other zooms in on the details (stories).

🔗 Embedded Mixed Methods Design

📌 What it is

• A research design where one method (quant OR qual) is the main approach, and the other is added inside (“embedded”) to give extra depth.
• Unlike sequential designs (done in phases), or concurrent designs (equal weight at the same time), embedded design clearly has a primary method and a secondary supporting method.

👉 Think of it like a main dish 🍝 with a side dish 🥗. One dominates, but the side adds flavor and depth.

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🛠 How it Works (Step by Step)

1. Choose the Primary Method
   • Decide if your study is mainly quantitative (numbers) or qualitative (stories).
2. Identify a Gap the Other Method Can Fill
   • Ask: What extra insight would a small piece of the other method add?
3. Collect the Embedded Data
   • Collect the main dataset first (e.g., survey, experiment, or long interviews).
   • Then add a small slice of the opposite method.
4. Analyze Both
   • Analyze the main dataset fully.
   • Use the embedded data to interpret, enrich, or validate the main findings.
5. Integrate Findings
   • Present results with the primary method first.
   • Then show how the embedded method adds nuance.

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🎯 When to Use

• When one method is clearly more important but needs extra support.
• When the study has limited time/resources for a full mixed methods design.
• When the main audience expects one approach (like funders want numbers), but you know stories add meaning.

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🧾 Practical Examples

🏫 Education Example

• Main (Quant): A school tests a new reading app with 200 students → tracks reading scores before and after (pre-test/post-test).
• Embedded (Qual): Interview 10 students to ask how they liked the app.
• Integration: Numbers show if scores improved. Stories explain why students found it motivating or boring.

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🏥 Healthcare Example

• Main (Qual): A researcher conducts in-depth interviews with 30 cancer survivors about their recovery journey.
• Embedded (Quant): Each participant also completes a short standardized scale measuring depression.
• Integration: The rich stories reveal emotional challenges, while the scale adds numeric evidence of mental health levels.

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🛍️ Business Example

• Main (Quant): A company runs a big customer satisfaction survey with 1,000 shoppers.
• Embedded (Qual): They also interview 15 shoppers to get detailed feedback.
• Integration: Survey results provide general satisfaction scores; interviews uncover specific frustrations with checkout speed.

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🌍 Community Example

• Main (Qual): Action research with a community about improving public transportation (interviews + focus groups).
• Embedded (Quant): A quick survey of 200 commuters about average travel times.
• Integration: Stories capture lived experiences, while the numbers provide hard evidence of delays.

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⚖️ Pros & Cons

Pros
✅ Flexible — easy to add a smaller dataset.
✅ Practical — doesn’t require equal weight to both methods.
✅ Strengthens your findings with an extra perspective.

Cons
❌ Risk that the embedded data looks “secondary” or less rigorous.
❌ Can be hard to integrate smoothly if results don’t align.
❌ Readers may question why one part was “small” compared to the other.

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👉 Beginner takeaway:
Embedded design = one main approach, one supporting approach.
It’s like baking a cake 🎂 (main method) but adding sprinkles on top (embedded method) to make it richer and more appealing.

🧾 Expanded Comparison of Mixed Methods Designs

Design	Order of Data Collection	Main Purpose	When to Use	Strengths	Limitations	Practical Example
Sequential Explanatory	Quant → Qual (numbers first, then stories)	To explain or interpret quantitative results	When you have strong survey/experiment results but need to understand why they look that way	Logical, easy to design; numbers guide follow-up questions; strong explanation of stats	Slower (two phases); requires skills in both methods; if qual findings don’t match quant, hard to integrate	A survey finds 65% of students stressed. Interviews later show causes: financial pressure, lack of sleep, workload.
Sequential Exploratory	Qual → Quant (stories first, then numbers)	To develop new ideas, frameworks, or theories, then test them	When little research exists; when you want to ground surveys in real experiences	Great for theory building; helps create valid surveys; combines depth + generalizability	Time-consuming; risk that quant survey oversimplifies rich qualitative data	Interviews with 15 business owners reveal 3 AI uses (automation, marketing, data analysis). A survey with 500 owners tests how common these uses are.
Concurrent (Parallel)	Quant + Qual at the same time	To compare, confirm, or enrich findings from two perspectives simultaneously	When you need efficiency or want to see if findings “match” (triangulation)	Saves time; provides complementary insights; allows cross-checking	Requires careful planning to run both well; hard to integrate if findings conflict	Survey shows 25% exercise regularly; focus groups at the same time reveal barriers (cost, time, safety). Together: low numbers explained by lived experience.
Embedded	One main (quant OR qual), other is secondary	To add depth, validation, or context to a primarily single-method study	When one method dominates (experiment, case study, survey), but you want extra support	Flexible; efficient; easy to integrate into a bigger project	Secondary method may feel less rigorous; risk of imbalance; interpretation can be tricky	A school runs a big reading app experiment (quant test scores). At the same time, 10 student interviews (qual) explain why students liked or disliked the app.

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