Necessary Condition Analysis (NCA): When a Condition Is Necessary but Not Sufficient
PLS-SEM asks which factors increase or decrease an outcome on average; NCA asks whether a high outcome is impossible when a condition remains below a minimum level.
NCA supports a necessity claim only when the empty upper-left region is meaningful, the effect size is substantial enough, permutation testing is appropriate and theory precedes the data.
A necessary condition is not a sufficient condition
- Theory
Specify the proposed necessary condition
- Ceiling line
Inspect the empty space and ceiling
- Evidence
Effect size and appropriate testing
- Bottleneck
Read X required for a target Y
Conceptual diagram. Conclusions depend on data range and measurement.
Source: Lê Minh · Conceptual illustration, not software output.1. A necessary condition is not a sufficient condition
If X is necessary for Y, a high Y cannot occur below a particular X threshold. High X nevertheless does not guarantee high Y because other conditions may remain unmet (Dul, 2016).
This asymmetric logic differs from regression coefficients. A small average effect can still constrain the attainable outcome; a large path coefficient does not itself establish necessity.
| Method | Question | Appropriate conclusion |
|---|---|---|
| PLS-SEM | How much does Y change on average as X changes? | Net effect/average trend |
| NCA | Does insufficient X prevent high Y? | Necessary minimum level |
2. Read the ceiling line, effect size and permutation test
The ceiling line describes the boundary of the observations. Effect size d expresses the empty-region proportion relative to the scope, but must be interpreted alongside measurement precision and data range.
A permutation test assesses whether the observed effect size exceeds what might arise randomly. A p-value does not replace effect magnitude or repair an unjustified hypothesis.
A narrow sample range, measurement scale, outliers or selection mechanism may produce an empty region. NCA requires theory, research design and robustness checks.
3. A cautious analysis workflow
Specify the necessity claim
Explain beforehand why insufficient X should constrain Y and identify the outcome level of interest.
Inspect the data
Check observed ranges, missingness, outliers and construct-score quality.
Estimate the ceiling line
Report CE-FDH or CR-FDH and its corresponding effect size.
Run permutations
Use sufficiently many permutations and report p-values alongside effect sizes.
Assess robustness
Consider plausible ceiling-line alternatives, influential observations and subgroup comparisons.
4. Reporting template and checklist
Reporting example in English
NCA indicated an effect size of d = [ ] for [X], estimated using [CE-FDH/CR-FDH], with permutation p = [ ]. Results suggested that [X] was a [degree] necessary condition for [Y level] within this sample and measurement range; this does not imply that [X] alone is sufficient to produce Y.
- Justify the necessity hypothesis before analysis.
- State the ceiling line and effect size.
- Report permutation testing.
- Do not equate necessity with a regression effect.
- Do not extrapolate beyond the observed range.
- Check robustness and influential observations.
Conclusion
NCA adds a useful managerial perspective: which factors must reach minimum levels before a high outcome is feasible. Its value lies in asymmetric logic and transparent limits on conclusions.
References
- Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of necessary but not sufficient causality. Organizational Research Methods, 19(1), 10–52.https://doi.org/10.1177/1094428115584005
- SmartPLS. (n.d.). Necessary Condition Analysis (NCA).https://smartpls.com/documentation/algorithms-and-techniques/nca/
Methodological note: Effect-size thresholds are guidelines. Prioritize theory, sample range and ceiling-line robustness.