Necessary condition analysis

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Necessary condition analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. [1] These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result. For example, the admission of a student into a Ph.D. program necessitates a prior degree; the progression of AIDS necessitates the presence of HIV; and organizational change necessitates communication.

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The absence these conditions guarantees the outcome cannot occur, and no other condition can overcome the lack of this condition. Further, necessary conditions are not always sufficient. For example, AIDS necessitates HIV, but HIV does not always cause AIDS. In such instances, the condition demonstrates its necessity but lacks sufficiency. NCA seeks to use statistical methods to test for such conditions.

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. [1] 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, [3] structural equation modelling, [4] [2] or qualitative comparative analysis, [5] [6] and derivative methods such as PLS-SEM and fsQCA. [7] [8] [9] Thus, scholars using NCA seek to reveal the necessary boundary conditions of causal conditions indicated by these other analytical techniques. [5] [9]

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. [1] 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 relationship [10]

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. [11] 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. [11]

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 invaluable. 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. [8]

Conclusion

NCA provides a framework for identifying the non-negotiable factors that must be present for a desired result. This methodology not only enriches our understanding of causal relationships but also guides 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.

Related Research Articles

In logic and mathematics, necessity and sufficiency are terms used to describe a conditional or implicational relationship between two statements. For example, in the conditional statement: "If P then Q", Q is necessary for P, because the truth of Q is guaranteed by the truth of P. Similarly, P is sufficient for Q, because P being true always implies that Q is true, but P not being true does not always imply that Q is not true.

A case study is an in-depth, detailed examination of a particular case within a real-world context. For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like world war, or more often the policy analysis of real-world problems affecting multiple stakeholders.

<span class="mw-page-title-main">Quantitative research</span> All procedures for the numerical representation of empirical facts

Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.

<span class="mw-page-title-main">Spurious relationship</span> Apparent, but false, correlation between causally-independent variables

In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third, unseen factor.

<span class="mw-page-title-main">Structural equation modeling</span> Form of causal modeling that fit networks of constructs to data

Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself.

<span class="mw-page-title-main">Data analysis</span> The process of analyzing data to discover useful information and support decision-making

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

<span class="mw-page-title-main">Confounding</span> Variable or factor in causal inference

In causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system.

<span class="mw-page-title-main">Research design</span> Overall strategy utilized to carry out research

Research design refers to the overall strategy utilized to answer research questions. A research design typically outlines the theories and models underlying a project; the research question(s) of a project; a strategy for gathering data and information; and a strategy for producing answers from the data. A strong research design yields valid answers to research questions while weak designs yield unreliable, imprecise or irrelevant answers.

<span class="mw-page-title-main">Causal model</span> Conceptual model in philosophy of science

In the philosophy of science, a causal model is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.

<span class="mw-page-title-main">Mediation (statistics)</span> Statistical model

In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable. Rather than a direct causal relationship between the independent variable and the dependent variable, which is often false, a mediation model proposes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.

In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common occurrence and can have a significant effect on the conclusions that can be drawn from the data.

<span class="mw-page-title-main">Quasi-experiment</span> Empirical interventional study

A quasi-experiment is an empirical interventional study used to estimate the causal impact of an intervention on target population without random assignment. Quasi-experimental research shares similarities with the traditional experimental design or randomized controlled trial, but it specifically lacks the element of random assignment to treatment or control. Instead, quasi-experimental designs typically allow the researcher to control the assignment to the treatment condition, but using some criterion other than random assignment.

In statistics, qualitative comparative analysis (QCA) is a data analysis based on set theory to examine the relationship of conditions to outcome. QCA describes the relationship in terms of necessary conditions and sufficient conditions. The technique was originally developed by Charles Ragin in 1987 to study data sets that are too small for linear regression analysis but large for cross-case analysis.

<span class="mw-page-title-main">Causation (sociology)</span> Belief that events occur in predictable ways and that one event leads to another

Causation refers to the existence of "cause and effect" relationships between multiple variables. Causation presumes that variables, which act in a predictable manner, can produce change in related variables and that this relationship can be deduced through direct and repeated observation. Theories of causation underpin social research as it aims to deduce causal relationships between structural phenomena and individuals and explain these relationships through the application and development of theory. Due to divergence amongst theoretical and methodological approaches, different theories, namely functionalism, all maintain varying conceptions on the nature of causality and causal relationships. Similarly, a multiplicity of causes have led to the distinction between necessary and sufficient causes.

Process tracing is a qualitative research method used to develop and test theories.. Process-tracing can be defined as the following: it is the systematic examination of diagnostic evidence selected and analyzed in light of research questions and hypotheses posed by the investigator. Process-tracing thus focuses on (complex) causal relationships between the independent variable(s) and the outcome of the dependent variable(s), evaluates pre-existing hypotheses and discovers new ones. It is generally understood as a "within-case" method to draw inferences on the basis of causal mechanisms, but it can also be used for ideographic research or small-N case-studies. It has been used in social sciences, as well as in natural sciences.

In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process.

The partial least squares path modeling or partial least squares structural equation modeling is a method for structural equation modeling that allows estimation of complex cause-effect relationships in path models with latent variables.

<span class="mw-page-title-main">SmartPLS</span> Software

SmartPLS is a software with graphical user interface for variance-based structural equation modeling (SEM) using the partial least squares (PLS) path modeling method. Users can estimate models with their data by using basic PLS-SEM, weighted PLS-SEM (WPLS), consistent PLS-SEM (PLSc-SEM), and sumscores regression algorithms. The software computes standard results assessment criteria and it supports additional statistical analyses . Since SmartPLS is programmed in Java, it can be executed and run on different computer operating systems such as Windows and Mac.

<span class="mw-page-title-main">WarpPLS</span>

WarpPLS is a software with graphical user interface for variance-based and factor-based structural equation modeling (SEM) using the partial least squares and factor-based methods. The software can be used in empirical research to analyse collected data and test hypothesized relationships. Since it runs on the MATLAB Compiler Runtime, it does not require the MATLAB software development application to be installed; and can be installed and used on various operating systems in addition to Windows, with virtual installations.

Biological tests of necessity and sufficiency refer to experimental methods and techniques that seek to test or provide evidence for specific kinds of causal relationships in biological systems. A necessary cause is one without which it would be impossible for an effect to occur, while a sufficient cause is one whose presence guarantees the occurrence of an effect. These concepts are largely based on but distinct from ideas of necessity and sufficiency in logic.

References

  1. 1 2 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.
  2. 1 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.
  3. ↑ "NCA and regression" (PDF). www.erim.eur.nl. Retrieved 20 September 2023.
  4. ↑ 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.
  5. 1 2 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.
  6. ↑ 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.
  7. ↑ 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.
  8. 1 2 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.
  9. 1 2 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.
  10. ↑ 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.
  11. 1 2 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.
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