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Necessary condition analysis
Quantitative data analytical technique

Necessary condition analysis (NCA) is a research method used to identify necessary conditions within datasets—crucial factors without which a specific outcome cannot occur. For instance, admission to a Ph.D. program requires a prior degree, the development of AIDS depends on the presence of HIV, and organizational change requires communication. While the absence of necessary conditions prevents the outcome, their presence alone does not guarantee it, meaning necessary conditions are not always sufficient. For example, HIV is necessary for AIDS but does not always cause it. NCA employs statistical techniques to test these indispensable but not always sufficient conditions, helping researchers better understand cause-effect relationships in complex phenomena.

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Overview

Traditional statistical methods often emphasize the identification of factors that are sufficient to produce an outcome.2 In contrast, NCA aims to uncover conditions that must be present for a specific outcome to occur.3 While researchers will sometimes use NCA as a stand-alone analysis, they often use it to add additional depth to existing analyses of data. For example, NCA acts as stand-alone method or as a complement to other analytical techniques such as regression-based analysis,4 structural equation modelling,56 or qualitative comparative analysis,78 and derivative methods such as PLS-SEM and fsQCA. 91011 Thus, scholars using NCA seek to reveal the necessary boundary conditions of causal conditions indicated by these other analytical techniques.1213

Methodology

NCA allows researchers to analyze how predictor variables constrain the outcome variable by revealing which predictor variables are considered to be necessary, and to what degree they constrain the outcome variable.14 This is done by evaluating the effect size d of each necessary condition, and examining the statistical significance of the necessary condition (permutation test), and by having theoretical justification for this type of a relationship15

Necessary condition analysis follows a step-by-step approach to identify necessary conditions. The key steps involved in conducting NCA are as follows:

  1. Formulation of a necessity hypothesis: The first step in NCA is to clearly define the theoretical expectation specifying the condition(s) that may be necessary for the outcome of interest. The outcome could be a specific event, achievement, or outcome that researchers want to understand better.
  2. Data collection: Relevant data about the conditions and the outcome are collected as the input to NCA. This data could be obtained through surveys, experiments, observations, or existing datasets, depending on the nature of the research.
  3. Identification of necessary conditions: NCA employs specific techniques to identify necessary conditions. These techniques include i) selection of ceiling line(s) in an XY plot and an evaluation of effect size d. ii) Performing a resampling procedure for examining the statistical significance of the necessary condition (permutation test). iii) Examination of the bottleneck table to specify the levels of the condition(s) that are necessary for particular levels of the outcome.
  4. Interpretation and validation: Once the necessary conditions are identified, researchers interpret the findings and validate them against existing theories or expert knowledge.16 This step helps ensure the robustness and reliability of the results.

Applications

Necessary condition analysis has found applications in a wide range of research areas. Some notable applications include:

  1. Business and management: NCA is used to identify the essential factors that are necessary for the success of a business, such as effective leadership, customer satisfaction, or employee engagement.
  2. Social sciences: In social sciences, NCA helps researchers understand the crucial conditions for various social phenomena, such as educational attainment, poverty reduction, or political stability.
  3. Engineering and manufacturing: NCA is employed to identify the minimum requirements for optimal performance or quality in engineering and manufacturing processes. It aids in determining the critical factors that must be met to achieve desired outcomes.17

Limitations

Necessary Condition Analysis (NCA) offers a nuanced perspective on data analysis by identifying conditions that must be present for a desired outcome to occur. However, its utility is bounded by several limitations that users must consider. Primarily, NCA's insights are limited by the quality and scope of the data used. If the data does not capture all relevant variables or is biased, the conclusions drawn about necessary conditions may be incomplete or misleading.

Moreover, NCA does not assert sufficiency; a condition deemed necessary might not be enough on its own to guarantee an outcome, necessitating a combination of conditions or further analysis to understand the full causal landscape. This characteristic means that NCA should be employed as part of a broader analytical strategy rather than a standalone method. It is most effective when used to complement other statistical techniques that explore sufficiency or when a clear hypothesis about necessity exists.

NCA's reliance on statistical significance also means it inherits the general limitations of statistical inference, including potential issues with sample size and the risk of overfitting. Consequently, results need to be interpreted with caution and, where possible, validated through additional empirical work or theoretical justification.

In contexts where identifying the bare minimum conditions for an outcome is critical — such as determining the essential factors for business success, key drivers of social phenomena, or minimum requirements in engineering processes — NCA can be valuable. However, its application is less suited to scenarios where the relationships between variables are predominantly sufficiency-based or where the causal dynamics are highly complex and interdependent.

Like other methods, the researcher needs to understand the meaning of the data and bring in the assumptions of the way they understand why thinks work the way they do to formulate relevant hypotheses and meaningful interpretations.18

Conclusion

NCA provides a framework for identifying the non-negotiable factors that must be present for a desired result. This methodology can improve understanding of causal relationships as well as guide decision-making by highlighting the minimum criteria that need to be met. However, it's important to recognize that necessary conditions, as identified by NCA, do not guarantee an outcome on their own; they simply establish the baseline requirements. Further analysis may be needed to uncover a combination of conditions that together are sufficient for the outcome.

The effectiveness of NCA is inherently linked to the quality of the data and the comprehensiveness of the variables considered. The approach requires careful interpretation of results and, ideally, should be used in conjunction with other analytical methods to build a more complete picture of causality.

References

  1. Dul, Jan (January 2016). "Necessary Condition Analysis (NCA): Logic and Methodology of "Necessary but Not Sufficient" Causality". Organizational Research Methods. 19 (1): 10–52. doi:10.1177/1094428115584005. hdl:1765/90024. ISSN 1094-4281. S2CID 54590079. http://journals.sagepub.com/doi/10.1177/1094428115584005 ↩

  2. Richter, Nicole Franziska; Schubring, Sandra; Hauff, Sven; Ringle, Christian M.; Sarstedt, Marko (1 January 2020). "When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA". Industrial Management & Data Systems. 120 (12): 2243–2267. doi:10.1108/IMDS-11-2019-0638. ISSN 0263-5577. S2CID 221493572. https://doi.org/10.1108/IMDS-11-2019-0638 ↩

  3. Dul, Jan (January 2016). "Necessary Condition Analysis (NCA): Logic and Methodology of "Necessary but Not Sufficient" Causality". Organizational Research Methods. 19 (1): 10–52. doi:10.1177/1094428115584005. hdl:1765/90024. ISSN 1094-4281. S2CID 54590079. http://journals.sagepub.com/doi/10.1177/1094428115584005 ↩

  4. "NCA and regression" (PDF). www.erim.eur.nl. Retrieved 20 September 2023. https://www.erim.eur.nl/fileadmin/centre_content/necessary_condition_analysis/Supplementary_Material_NCA_and_regression_20210604.pdf ↩

  5. Sukhov, Alexandre; Olsson, Lars E.; Friman, Margareta (April 2022). "Necessary and sufficient conditions for attractive public Transport: Combined use of PLS-SEM and NCA". Transportation Research Part A: Policy and Practice. 158: 239–250. doi:10.1016/j.tra.2022.03.012. S2CID 247408865. https://doi.org/10.1016%2Fj.tra.2022.03.012 ↩

  6. Richter, Nicole Franziska; Schubring, Sandra; Hauff, Sven; Ringle, Christian M.; Sarstedt, Marko (1 January 2020). "When predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA". Industrial Management & Data Systems. 120 (12): 2243–2267. doi:10.1108/IMDS-11-2019-0638. ISSN 0263-5577. S2CID 221493572. https://doi.org/10.1108/IMDS-11-2019-0638 ↩

  7. Dul, Jan (1 April 2016). "Identifying single necessary conditions with NCA and fsQCA". Journal of Business Research. Set-Theoretic research in business. 69 (4): 1516–1523. doi:10.1016/j.jbusres.2015.10.134. ISSN 0148-2963. https://www.sciencedirect.com/science/article/pii/S0148296315005573 ↩

  8. Vis, Barbara; Dul, Jan (November 2018). "Analyzing Relationships of Necessity Not Just in Kind But Also in Degree: Complementing fsQCA With NCA". Sociological Methods & Research. 47 (4): 872–899. doi:10.1177/0049124115626179. ISSN 0049-1241. PMC 6195096. PMID 30443090. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195096 ↩

  9. Richter, Nicole F.; Hauff, Sven; Ringle, Christian M.; Gudergan, Siegfried P. (19 July 2022). "The Use of Partial Least Squares Structural Equation Modeling and Complementary Methods in International Management Research". Management International Review. 62 (4): 449–470. doi:10.1007/s11575-022-00475-0. ISSN 0938-8249. https://doi.org/10.1007%2Fs11575-022-00475-0 ↩

  10. Sukhov, Alexandre; Friman, Margareta; Olsson, Lars E. (1 September 2023). "Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making". Journal of Retailing and Consumer Services. 74: 103424. doi:10.1016/j.jretconser.2023.103424. ISSN 0969-6989. https://doi.org/10.1016%2Fj.jretconser.2023.103424 ↩

  11. Sukhov, Alexandre; Olsson, Lars E.; Friman, Margareta (April 2022). "Necessary and sufficient conditions for attractive public Transport: Combined use of PLS-SEM and NCA". Transportation Research Part A: Policy and Practice. 158: 239–250. doi:10.1016/j.tra.2022.03.012. ISSN 0965-8564. https://doi.org/10.1016%2Fj.tra.2022.03.012 ↩

  12. Dul, Jan (1 April 2016). "Identifying single necessary conditions with NCA and fsQCA". Journal of Business Research. Set-Theoretic research in business. 69 (4): 1516–1523. doi:10.1016/j.jbusres.2015.10.134. ISSN 0148-2963. https://www.sciencedirect.com/science/article/pii/S0148296315005573 ↩

  13. Sukhov, Alexandre; Olsson, Lars E.; Friman, Margareta (April 2022). "Necessary and sufficient conditions for attractive public Transport: Combined use of PLS-SEM and NCA". Transportation Research Part A: Policy and Practice. 158: 239–250. doi:10.1016/j.tra.2022.03.012. ISSN 0965-8564. https://doi.org/10.1016%2Fj.tra.2022.03.012 ↩

  14. Dul, Jan (January 2016). "Necessary Condition Analysis (NCA): Logic and Methodology of "Necessary but Not Sufficient" Causality". Organizational Research Methods. 19 (1): 10–52. doi:10.1177/1094428115584005. hdl:1765/90024. ISSN 1094-4281. S2CID 54590079. http://journals.sagepub.com/doi/10.1177/1094428115584005 ↩

  15. Dul, Jan; van der Laan, Erwin; Kuik, Roelof (23 August 2018). "A Statistical Significance Test for Necessary Condition Analysis". Organizational Research Methods. 23 (2): 385–395. doi:10.1177/1094428118795272. hdl:1765/110341. ISSN 1094-4281. S2CID 169957834. https://dx.doi.org/10.1177/1094428118795272 ↩

  16. Dul, Jan; Hauff, Sven; Bouncken, Ricarda B. (22 March 2023). "Correction: Necessary condition analysis (NCA): review of research topics and guidelines for good practice". Review of Managerial Science. 17 (4): 1535–1537. doi:10.1007/s11846-023-00634-z. ISSN 1863-6683. https://doi.org/10.1007%2Fs11846-023-00634-z ↩

  17. Dul, Jan; Hauff, Sven; Bouncken, Ricarda B. (22 March 2023). "Correction: Necessary condition analysis (NCA): review of research topics and guidelines for good practice". Review of Managerial Science. 17 (4): 1535–1537. doi:10.1007/s11846-023-00634-z. ISSN 1863-6683. https://doi.org/10.1007%2Fs11846-023-00634-z ↩

  18. Sukhov, Alexandre; Friman, Margareta; Olsson, Lars E. (1 September 2023). "Unlocking potential: An integrated approach using PLS-SEM, NCA, and fsQCA for informed decision making". Journal of Retailing and Consumer Services. 74: 103424. doi:10.1016/j.jretconser.2023.103424. ISSN 0969-6989. https://doi.org/10.1016%2Fj.jretconser.2023.103424 ↩