alterlab-ieu/alterlab-academic-skills/alterlab-mixed-methods

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?"

Notation: QUAN → qual → Interpretation
 
Timeline:
  Phase 1: Quantitative data collection & analysis
  Phase 2: Identify results needing explanation
           (outliers, unexpected findings, significant predictors)
  Phase 3: Qualitative data collection targeting those results
  Phase 4: Qualitative analysis
  Phase 5: Integration and interpretation
 
Connection point: Quantitative results inform qualitative sampling and protocols
 
Example research question:
  "What factors explain why some departments showed significant improvement
   in research output while others with similar resources did not?"

# Conceptual selection logic for explanatory sequential design
def select_qualitative_participants(quant_results):
    """
    Common strategies for selecting participants
    for the qualitative follow-up phase.
    """
    strategies = {
        "extreme_cases": "Select participants at the extremes of the distribution",
        "outlier_cases": "Select participants whose outcomes deviate from predictions",
        "typical_cases": "Select participants near the mean for each subgroup",
        "maximal_variation": "Select across the full range of the key variable",
        "confirming_disconfirming": "Select cases that support and challenge quant findings",
        "subgroup_follow_up": "Select from each statistically identified subgroup"
    }
    # Example: Follow up on regression residuals
    high_residual = quant_results[quant_results['residual'].abs() > 2.0]
    typical = quant_results[quant_results['residual'].abs() < 0.5].sample(n=5)
    return {
        "outliers": high_residual,
        "typical": typical,
        "rationale": "Comparing outlier and typical cases to explain model misfit"
    }

Notation: qual → QUAN → Interpretation
 
Timeline:
  Phase 1: Qualitative data collection & analysis
  Phase 2: Develop quantitative instrument/variables from qual findings
           (e.g., survey items derived from interview themes)
  Phase 3: Quantitative data collection & analysis
  Phase 4: Integration and interpretation
 
Connection point: Qualitative findings generate hypotheses, variables, or instruments
                  for the quantitative phase
 
Common applications:
  - Instrument development (qual themes → survey items → psychometric validation)
  - Taxonomy development (qual categories → quantitative classification testing)
  - Theory generation (qual grounded theory → quantitative hypothesis testing)

Qualitative Phase:
  Interviews (n=20-30) → Thematic analysis → Identify constructs and language
 
Connecting Bridge:
  Themes → Item pool → Expert review → Cognitive interviews → Pilot items
 
Quantitative Phase:
  Survey (n=300+) → Exploratory factor analysis → Confirmatory factor analysis
  → Reliability testing → Validity evidence (convergent, discriminant, criterion)

Notation: QUAN(qual) — Quantitative primary with embedded qualitative
 
Example: Randomized controlled trial with embedded qualitative process evaluation
 
  ┌─────────────────────────────────────────────┐
  │  RCT (Primary Quantitative Design)          │
  │                                              │
  │  Treatment group ──→ Outcome measures        │
  │       │                                      │
  │       └──→ [Qualitative interviews at        │
  │             midpoint to understand            │
  │             implementation fidelity           │
  │             and participant experience]       │
  │                                              │
  │  Control group ──→ Outcome measures          │
  └─────────────────────────────────────────────┘
 
Purpose: The qualitative strand answers a secondary question
         within the larger quantitative framework

Key principles:
  1. Research questions address power, oppression, or social justice
  2. Marginalized community members participate in design decisions
  3. Methods are selected to amplify silenced voices
  4. Integration explicitly examines how findings relate to structural inequity
  5. Results include action agendas and recommendations for change
 
Diagram: label the framework around the procedural diagram, e.g.
  Transformative framework: QUAL → QUAN
  Transformative framework: QUAL + QUAN
(Square brackets are reserved for a project within a series of studies — see §2.)

Notation: Study 1 (QUAN) → Study 2 (qual) → Study 3 (QUAL + QUAN) → ...
 
Example: Multi-year curriculum evaluation program
  Year 1: Needs assessment (QUAL → QUAN)
  Year 2: Pilot intervention (QUAN with embedded qual)
  Year 3: Full-scale RCT (QUAN → qual for process evaluation)
  Year 4: Sustainability study (QUAL + QUAN convergent)

Symbol Reference:
  UPPERCASE   = Priority/emphasis strand (e.g., QUAN = quantitative is primary)
  lowercase   = Secondary/supplementary strand (e.g., qual = qualitative is secondary)
  +           = Concurrent/simultaneous collection
  →           = Sequential collection (left happens before right)
  ( )         = Strand embedded within a larger design, e.g. QUAN(qual)
  →←          = Recursive/iterative strands (Nastasi et al., 2007)
  [ ]         = A mixed methods project within a series of studies,
                e.g. QUAL → QUAN → [QUAN + qual] (Morse & Niehaus, 2009)
  =           = Purpose of the mixing, e.g. QUAN → qual = explain
 
Common patterns:
  QUAN + QUAL      Convergent, equal weight
  QUAN → qual      Explanatory sequential, quantitative dominant
  qual → QUAN      Exploratory sequential, quantitative dominant
  QUAN(qual)       Embedded qualitative within quantitative