alterlab-mixed-methods

name	alterlab-mixed-methods
description	Mixed methods research design and integration strategies for combining qualitative and quantitative approaches. Use when planning convergent, explanatory sequential, exploratory sequential, embedded, transformative, or multiphase designs; when integrating diverse data sources through merging, connecting, or embedding; when constructing joint displays or meta-inferences; or when evaluating quality criteria specific to mixed methods research. Covers Creswell & Plano Clark frameworks, notation systems, and software tools for integration. For single-strand qualitative coding (thematic analysis, grounded theory, saturation, inter-coder reliability) use alterlab-qualitative-methods; for questionnaire/Likert/instrument-validation mechanics use alterlab-survey-design. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read WebFetch WebSearch Bash(python:*)
compatibility	No API key required. Guidance-focused skill; uses WebFetch/WebSearch and optional Python helpers via `uv run python`.
metadata	{"skill-author":"AlterLab","version":"1.0.0","last_updated":"2026-03-18"}

Mixed Methods Research Design

Overview

Mixed methods research is a methodology that combines quantitative and qualitative research approaches within a single study or program of inquiry. It goes beyond simply collecting both types of data — it requires intentional integration at one or more stages of the research process (design, methods, interpretation, reporting) to generate insights that neither approach could produce alone. This skill provides comprehensive guidance on designing, executing, and reporting mixed methods studies according to established frameworks, with particular emphasis on the Creswell & Plano Clark typology and the integration strategies that distinguish rigorous mixed methods from mere parallel data collection.

Mixed methods emerged as a recognized "third methodological movement" in the early 2000s, though researchers had been combining approaches for decades. Its legitimacy rests on the philosophical position of pragmatism — selecting methods based on what works best to answer the research questions rather than adhering to a single paradigm. Today, mixed methods is a requirement or strong recommendation in many funding agencies (NIH, NSF, ESRC) and is increasingly expected in fields such as health sciences, education, evaluation research, and social policy.

When to Use This Skill

Use this skill when:

• Designing a study that requires both statistical generalizability and contextual depth
• Planning how to combine qualitative and quantitative strands in a coherent design
• Choosing between convergent, explanatory sequential, exploratory sequential, embedded, transformative, or multiphase designs
• Developing integration strategies (merging, connecting, embedding) for multi-strand studies
• Constructing joint displays to visualize integrated findings
• Formulating meta-inferences that go beyond what either strand alone could produce
• Writing the methods section of a mixed methods manuscript or proposal
• Evaluating the quality, legitimacy, or rigor of a mixed methods study
• Using the Creswell & Plano Clark notation system to diagram a design
• Selecting software tools for managing mixed methods data integration
• Preparing a mixed methods study for IRB review with multiple data collection phases
• Responding to reviewer critiques about the rationale for mixing methods

Core Capabilities

1. Mixed Methods Design Typology

The following designs represent the major archetypes. In practice, many studies adapt or combine these.

Convergent Parallel Design (QUAL + QUAN)

Both strands are collected and analyzed concurrently, then merged for interpretation.

Notation: QUAL + QUAN → Merge → Interpretation

Timeline:
  Phase 1 (concurrent):
    ├── Quantitative data collection & analysis
    └── Qualitative data collection & analysis
  Phase 2:
    └── Merge results → Compare, contrast, synthesize

Purpose: Triangulation, complementarity, or obtaining a more complete understanding

Example research question:
  "How do survey measures of teacher self-efficacy converge with or diverge
   from teachers' narrative accounts of their classroom confidence?"