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Causal inference & experimentsCausal inferenceSet theoryMultiple pathwaysEquifinalityMedium N

Qualitative Comparative Analysis

Qualitative Comparative Analysis
Also known as: QCA、Boolean analysis、Set-theoretic comparative analysis

Compares combinations of conditions and outcomes across cases. Calibrates set membership with evidence and distinguishes necessary from sufficient conditions. [1]

QCA design and calibration worksheet — openReferences and checked sections
Difficulty
Advanced
Cost
Medium
Duration
Illustrative planning durations, not common standards: design/preparation 1–2 months, analysis/interpretation 2–4 weeks
Content updated: 2026-09-13Source checked: 2026-09-13 (includes AI cross-checking)No documented human reviewIncludes fictional examples

How it works at a glance

Define condition/outcome sets: crisp membership is present/absent; fuzzy membership has degrees. Organize combinations in truth tables and examine contradictions and unobserved combinations. [1]

Consistency describes set-relationship coherence; coverage describes empirical extent. No fixed cutoff proves causality; return to case evidence. [1]

Which combinations of conditions produce the outcome? Examine configurations, not isolated factors.Condition A＊Condition B＊Condition CCondition A＊Condition D＊Not EOutcome (success)Several paths may lead here* = AND / paths 1 and 2 = OR
Use set theory to analyze combinations of conditions and outcomes across cases. Identify multiple paths to the same outcome: equifinality.

When to use it and when it is unsuitable

When to use it

  • Compare about 10–50 countries, organizations or sites to identify combinations associated with success/failure
  • Systematically compare successes/failures for lessons about important conditions
  • Multiple combinations may achieve the same result: equifinality
  • Too few cases for regression but too many for process tracing

When it is unsuitable

  • Cannot maintain set definitions/case knowledge or explain calibration/combinations
  • Linear average variable-effect estimates are required
  • No theoretical/empirical grounds justify membership or calibration

Participants, roles, and deliverables

Participants and roles

1–2 trained evaluators/researchers using R QCA/SetMethods or fs/QCA; domain expertise is essential for calibration

Main deliverables

  • Truth table: condition combinations × cases × outcomes
  • Sufficient solutions and consistency/coverage
  • Case-solution correspondence
  • Causal pathway interpretation and policy implications

Implementation steps

  1. Design cases, conditions and outcomes
    Define scope and sets from theory/case knowledge. More conditions create more combinations; limit according to observed diversity.
  2. Calibrate variables
    csQCA uses 0/1 membership. fsQCA sets evidence-based raw-data anchors for full membership, crossover and non-membership and records method. Do not equate with simple numerical normalization.
  3. Build truth tables
    Organize combinations, case counts and consistency. Distinguish contradictory outcomes within combinations from logical remainders without cases; explain thresholds/treatment.
  4. Analyze necessity
    Check whether a condition or its negation holds in all outcome cases. Assess importance through consistency and coverage.
  5. Analyze sufficiency through Boolean minimization
    Separate sufficiency from necessity and simplify combinations. Explain differing remainder assumptions in complex, intermediate and parsimonious solutions; judge against theory/cases.
  6. Interpret solutions against cases
    Report consistency/coverage and vary settings/check cases. Do not universally impose 0.80 or 0.25; explain criteria fitting necessity/sufficiency, calibration and distribution.

Strengths and limitations

Strengths

  • Organizes case comparisons as set relations focusing on combinations and pathways
  • Bridges quantitative/qualitative approaches for medium N
  • Necessity/sufficiency translates policy implications such as no success without a condition or success with a combination
  • Transparent truth tables/solutions support replication and critique

Limitations

  • Credibility depends on calibration judgments; different settings may yield different conclusions
  • Causal direction/timing is not necessarily established; theoretical/descriptive ordering checks needed
  • Logical necessity/sufficiency does not prove mechanisms; QCA alone insufficiently distinguishes association/causality
  • Sparse combinations create many remainders and sensitive solutions; fixed case counts cannot determine suitability.

Common pitfalls

  • Unjustified subjective calibration, such as intuitive 0.5, undermines credibility. Always record/report anchors from substantive thresholds, prior research or expertise.
  • Distinguish solution types and report remainders/counterfactual choices; no solution is automatically best.
  • High consistency with low coverage explains little of outcomes; report both.
  • Do not automatically infer causality from sets. Check timing, theory, evidence and alternatives.

Conditions of use and evidence

  • Distinguish combinations, necessity/sufficiency and calibration grounds; separately examine absent outcomes. [1]
Fictional example; not actual results

Organize plan use and workplace conditions as sets

A fictional crisp-set example of 4 cases: A=discussion meeting, B=staff continuity, Y=plan use.

CaseABY
Organization 1111
Organization 2111
Organization 3100
Organization 4010

Common errors in judgment

Common mistake

Mechanically split conditions at their mean into 0/1

How to revise

Calibrate from conceptual boundaries and case evidence

ReasonScale numbers differ from meaning of membership.

Common mistake

Ignore other combinations/counterexamples because consistency is high

How to revise

Check contradictions and unobserved combinations

ReasonResults depend on selection and simplification assumptions.

Organize your thinking by writing

QCA design and calibration worksheet

A practical template is available to fill in and print on A4 paper.

Open worksheet

Related methods

Process TracingContribution AnalysisCase StudyMixed Methods ResearchRealist Evaluation

References and checked sections

  1. Qualitative comparative analysis
    BetterEvaluation
    Section consulted
    Qualitative Comparative Analysis / Causal Pathway features
    How it relates to the text
    Distinguish combinations, necessity/sufficiency and calibration grounds; separately examine absent outcomes.
    Source checked: (includes AI cross-checking)

Editorial and checking information

Content updated: Source checked: (includes AI cross-checking)No documented human reviewIncludes fictional examplesEditorial policy and scope of checkingCite this pageReport a correction

How to cite this page

EvalCompass Editorial Team. “Qualitative Comparative Analysis”. EvalCompass, updated 2026-09-13. https://evalcompass.jun-nakatani.com/en/methods/qca/
Add the date you accessed the page. Text created by this site is available under CC BY 4.0. Quotations, external materials, and bundled fonts have their own terms of use. When reusing content, credit the source, link to the license, and indicate any changes.

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Change history
  • — Checked sources for definitions and conditions and added fictional examples, judgment rationales and completed entries for every field. Revised unsupported historical or uptake claims. Reviewed procedures and cautions, adjusting fixed counts, thresholds and institutional generalizations. Preserved field positions, types and counts while revising fixed conditions and thresholds.