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mixed-methods

Design mixed methods studies with joint displays and convergence codes.

Updated Aug 23, 2026
Search Tags:#integration#research design#triangulation#mixed methods#sample size planning#joint display#qualitative and quantitative
One-click install
npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill mixed-methods-yuuqq
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mixed-methods
Source: https://github.com/Yuuqq/claude-social-science-skills/tree/main/social-science-skills/mixed-methods
Command: npx skills add https://github.com/Yuuqq/claude-social-science-skills --skill mixed-methods-yuuqq

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Mixed methods research is difficult to plan and justify because it requires coordinating quantitative and qualitative strands while producing claims that are genuinely integrated rather than merely parallel.

Core Features & Use Cases

  • Design selection: Choose convergent, explanatory sequential, exploratory sequential, embedded, or multiphase designs based on priority and timing.
  • Integration strategies: Use joint displays (merging), connecting across phases, and embedding one strand within the other to produce meta-inferences.
  • Triangulation + quality control: Systematically code convergence (confirmation/expansion/discordance/complementary) and apply legitimation and reporting checks (e.g., MMIRA, Fetters/Creswell).
  • Operational workflow support: Includes a runnable Python demo that illustrates joint display construction and convergence-based integration assessment.

Quick Start

Use the mixed-methods skill to design a convergent study, create a joint display table mapping each quantitative result to qualitative themes, and generate meta-inferences from the convergence codes.

Frequently Asked Questions about mixed-methods

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate qualitative and quantitative research strands in a mixed methods study?▼

To integrate qualitative and quantitative strands in mixed methods research, use joint displays to merge results, connect phases sequentially, or embed strands, then generate meta-inferences from systematic convergence coding.

What is the best way to design a convergent mixed methods study?▼

The best way to design a convergent mixed methods study is to coordinate data collection timing, select appropriate sampling and integration points, and map quantitative results to qualitative themes using joint displays with convergence codes.

How do I code triangulation results for confirmation, expansion, or discordance?▼

Code triangulation results for confirmation, expansion, discordance, or complementary outcomes by applying legitimation and reporting checks from frameworks like MMIRA or Fetters/Creswell to systematically assess convergence.

Can I use Python to build joint displays and assess convergence for mixed methods research?▼

Yes, you can use Python to build joint displays and assess convergence. The skill includes a runnable Python demo using numpy, pandas, and scipy to illustrate joint display construction and triangulation analysis.

When should I choose an explanatory sequential design over an exploratory sequential design?▼

Choose an explanatory sequential design when prioritizing quantitative data collection first followed by qualitative exploration, and an exploratory sequential design when starting with qualitative exploration to inform subsequent quantitative phases.

What are the limitations of using mixed methods research design?▼

Limitations of mixed methods research design include the difficulty of justifying coordinated strands and producing genuinely integrated claims rather than merely parallel qualitative and quantitative findings.

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