Order the printed book (Routledge)
Cover
Preface
Acknowledgments
1
Introduction
Part I. Principles
2
Causality
2.1
Summary of this chapter
2.2
What is a cause?
2.3
Sufficiency causality
2.3.1
Deterministic sufficiency
2.3.2
Probabilistic sufficiency
2.3.3
Typicality sufficiency
2.4
Necessity causality
2.4.1
Deterministic necessity
2.4.2
Probabilistic necessity
2.4.3
Typicality necessity
2.5
Causal versus conditional logic
2.6
From binary necessity to continuous necessity
2.7
Causal underdetermination
3
Theory
3.1
Summary of this chapter
3.2
Main elements of a theory
3.3
Parsimonious necessity theories
3.4
Types of necessity theories
3.5
Direction of a necessity relationship
3.6
Expected empty corner
3.7
Double necessity
3.8
The meaning of ‘necessary but not sufficient’
4
Mathematics
4.1
Summary of this chapter
4.2
Branches of mathematics
4.3
Model specification
4.3.1
Bounding box
4.3.2
Ceiling line
4.3.3
Model degrees of freedom
4.4
Mathematical estimation
4.4.1
Effect size
4.4.2
Necessity inefficiency
4.5
Model fit
4.5.1
Complexity
4.5.2
Fit
4.5.3
Ceiling accuracy
4.5.4
Purity – noise and exceptions
4.5.5
Solidity – support and spread
4.5.6
Sharpness
4.5.7
NCA ribbon
4.5.8
Summary model fit metrics
4.6
Multiple necessary conditions
4.6.1
Formalization of single condition approach
4.6.2
Illustration
4.6.3
Arguments for single condition analyses
4.6.4
Chain of necessity
5
Statistics
5.1
Summary of this chapter
5.2
Sampling logic
5.3
Permutation tests
5.3.1
General
5.3.2
Assumptions for NCA’s permutation tests
5.3.3
Null test: effect size difference from 0
5.3.4
Contrast test: effect size difference between two conditions
5.3.5
Independent test: effect size difference between two groups
5.3.6
Paired test: effect size difference between two time points
5.3.7
Common scope
5.4
Statistical modeling
5.5
Quality of ceiling lines
5.5.1
Statistical quality criteria
5.5.2
Simulation results for NCA’s effect size
5.6
Power analysis
5.7
Reverse causality
5.8
Measurement error
5.9
Spuriousness
5.9.1
Lack of theoretical support
5.9.2
Low-quality data
6
Credibility
6.1
Summary of this chapter
6.2
Core criteria for necessity
6.3
Statistical credibility
6.3.1
True Positive Rate (TPR, sensitivity)
6.3.2
True Negative Rate (TNR, specificity)
6.4
Empirical credibility
6.4.1
Robustness
6.4.2
Model fit
6.5
Recommendations for evaluating credibility
Part II. Application
7
Hypothesis
7.1
Summary of this chapter
7.2
Hypothesis formulation and testing
7.3
Why developing necessity hypotheses is different
7.4
Knowledge sources for developing necessity hypotheses
7.4.1
Explicit knowledge
7.4.2
Tacit knowledge
7.5
Developing a formal necessity hypothesis
7.5.1
Step 1: Define a preliminary necessity theory
7.5.2
Step 2: Conduct thought experiments
7.5.3
Step 3: Provide causal explanation
8
Data
8.1
Summary of this chapter
8.2
Study design
8.2.1
Observational study
8.2.2
Longitudinal study
8.2.3
Case study
8.2.4
Experimental study
8.3
Sampling and selection of cases
8.3.1
Large-n sampling
8.3.2
Small-n case selection
8.3.3
Replication
8.4
Measurement
8.4.1
Quantitative data
8.4.2
Qualitative data
8.4.3
Set membership data
8.5
Dataset
8.5.1
Archival data
8.5.2
Data transformation
9
Data analysis
9.1
Summary of this chapter
9.2
Illustrative example
9.3
Model specification
9.3.1
Bounding box specification
9.3.2
Ceiling line specification
9.4
Selection of evaluation criteria
9.4.1
Effect size threshold
9.4.2
\(p\)
-value threshold
9.4.3
Selection of target outcome
9.5
Visual inspection of the
\(XY\)
-plot
9.5.1
Is the expected corner empty?
9.5.2
Is the selected ceiling line appropriate?
9.5.3
Which cases are possible outliers?
9.5.4
What is the data pattern in the rest of the plot?
9.6
Ceiling line estimation
9.7
Effect size and
\(p\)
-value estimation
9.8
Outlier analysis
9.8.1
Outlier types
9.8.2
Outlier identification
9.8.3
Outlier decision approach
9.9
Model fit
9.9.1
Complexity
9.9.2
Fit
9.9.3
Ceiling accuracy
9.9.4
Noise
9.9.5
Exceptions
9.9.6
Support
9.9.7
Spread
9.9.8
Sharpness
9.9.9
Evaluation of model fit
9.10
Conclusion about necessity-in-kind
9.11
Bottleneck analysis: necessity-in-degree
9.11.1
Levels expressed as actual values
9.11.2
Levels expressed as percentiles
9.11.3
Multiple necessary conditions
9.11.4
Interpretation of the bottleneck table for other corners
9.11.5
NN and NA in the bottleneck table
9.12
Robustness checks
9.12.1
Different ceiling line choice
9.12.2
Different choice of the scope
9.12.3
Different choice of the threshold level of the effect size
9.12.4
Different choice of the threshold level of the
\(p\)
-value
9.12.5
Different outlier choice
9.12.6
Different choice of the target outcome
9.12.7
Robustness table
9.13
Meta-analysis
10
Reporting
10.1
Summary of this chapter
10.2
Types of NCA publications
10.2.1
Empirical publication
10.2.2
Theoretical publication
10.2.3
Methodological introduction publication
10.2.4
Practical publication
10.3
SCoRe checklist and recommendations
10.3.1
Introduction/General
10.3.2
Theory/Hypotheses
10.3.3
Methods – data
10.3.4
Methods – data analysis
10.3.5
Results
10.3.6
Discussion
11
Multimethod studies
11.1
Summary of this chapter
11.2
Method-triangulation versus causal-pluralism
11.3
NCA with regression-based methods
11.3.1
Principles of regression analysis
11.3.2
Comparison NCA and regression-based methods
11.3.3
Regression-based statistical concepts do not (always) apply to NCA
11.3.4
Combining NCA and regression-based methods
11.4
Combining NCA and SEM in a multimethod study
11.4.1
Steps for conducting NCA with SEM
11.4.2
Recommendations for combining NCA and SEM
11.5
NCA with QCA
11.5.1
Principles of QCA
11.5.2
Comparison NCA and QCA
11.5.3
Combining NCA and QCA
11.6
Combining NCA and QCA in a multimethod study
11.6.1
Steps for conducting NCA with QCA’s necessity analysis
11.6.2
Example NCA-QCA method-triangulation multimethod study
11.6.3
Steps for conducting NCA with QCA’s sufficiency analysis
11.6.4
Example NCA-QCA causal-pluralism multimethod study
11.6.5
Recommendations for combining NCA and QCA
12
NCA in practice
12.1
Summary of this chapter
12.2
The practical value of necessity logic
12.2.1
Must-have factors for desired outcomes
12.2.2
Stop factors for undesired outcomes
12.2.3
Shifting attention from average to individual interventions
12.2.4
Examples of application areas
12.3
NCA’s contribution to change and innovation
12.3.1
Part 1: Intervention strategy
12.3.2
Part 2: Intervention design
12.4
Interventions for making desired outcomes possible
12.4.1
Intervention effort
12.4.2
Intervention effectiveness, waste and efficiency
12.4.3
Ideal intervention
12.5
Interventions for preventing undesired outcomes
12.6
Interventions with multiple necessary conditions
12.7
Illustrative example
12.7.1
Example Part 1: Intervention strategy
12.7.2
Example Part 2: Intervention design
Summary and personal reflection
Summary of the book
Reflections on a decade of NCA
Part III. Additional materials
Appendix
A
Nomenclature and glossary
A.1
Nomenclature
A.2
Glossary
B
Software
B.1
NCA
with
R
B.2
Additional R-code
B.3
Demonstration of
NCA
with
R
Example
B.3.1
Stage 1: Formulate the formal necessity hypothesis
B.3.2
Stage 2. Collect data
Install and load the NCA package and data
B.3.3
Stage 3. Analyze data
Advanced functions
B.3.4
Stage 4. Report results
B.4
Other software packages with NCA
B.4.1
Demonstration of
nca
with
Stata
C
Publications
C.1
Journal articles: NCA alone
C.2
Journal articles: NCA and MLR
C.3
Journal articles: NCA and CB-SEM
C.4
Journal articles: NCA and PLS-SEM
C.5
Journal articles: NCA and QCA
C.6
Journal articles: NCA and other methods
C.7
Journal articles: NCA and a combination of other methods
D
Affine transformations
E
Simulations for TPR and TNR
E.1
True Positive Rate (TPR)
E.2
True Negative Rate (TNR)
F
Necessity and the traditional experiment
F.1
Traditional experiment
F.2
Necessity lessons from the traditional experiment
G
Correlation by necessity
Situation
Symmetry transformation
Correlation
H
Demonstration NCA with PLS-SEM
H.1
Step 1: Theorize necessity and probabilistic relationships
H.2
Step 2: Collect data
H.3
Step 3: Conduct SEM
H.4
Step 4: Extract latent variable scores
H.5
Step 5: Conduct NCA
H.6
Step 6: Produce NERT
H.7
Step 7: Produce BIPMA
H.8
Step 8: Interpret results
I
Bottleneck distance table
Bibliography
Version history
Version 2.0.1 (5 October 2026)
Version 2.0.0 (September 24, 2026)
Version 1.0.0 (March 10, 2026)
Book extras
NCA website
Send email to the author
Necessary Condition Analysis (NCA)
Part III. Additional materials