Understanding the causal mechanisms underlying the development of mental disorders and their symptoms is essential for advancing effective prevention and treatment strategies. However, research in this field has predominantly relied on sufficiency logic within a probabilistic framework, coupled with traditional statistical methods (i.e., multiple linear regression, Structural Equation Modelling, etc.) where risk factors are associated with an increased likelihood of developing a disorder. While valuable, this approach also carries inherent assumptions and limitations. Additionally, the crucial concept of causal necessity has been largely overlooked. By integrating necessity logic within a deterministic framework—where the absence of a necessary risk factor prevents the development of a disorder in nearly everyone— we propose a novel and promising approach, exemplified by Necessary Condition Analysis (NCA). In this paper, we outline the theoretical foundations of NCA and illustrate its potential for advancing mental health research, with a specific application to the Interpersonal Theory of Suicide. We also discuss how NCA can address critical challenges in mental health science, refine existing methodologies, and open new pathways for enhancing both research and clinical practice.

Objectives Being exposed to adverse psychosocial working conditions contributes to poor mental health in young workers. This study explores whether psychosocial work adversities are a necessary condition for work-related emotional exhaustion in young workers.Design Data from the ‘Netherlands Working Condition Survey 2021’ was used. By applying a novel method called Necessary Condition Analysis, we tested two psychosocial work adversities as necessary conditions for high work-related emotional exhaustion in young workers: (1) a composite score of high job demands and low job resources and (2) a composite score of high job demands. Additionally, we tested whether the threshold for job demands as a necessary condition for high work-related emotional exhaustion differed for young workers with low versus high resources.Setting Secondary data analysis on a national working population-based survey.Participants The sample included 5791 young workers in the Netherlands (aged <30 years; 56.8% female).Primary outcome measure Work-related emotional exhaustion.Results A high level of the composite on job demands and job resources is necessary for a high level of work-related emotional exhaustion in young workers (effect size=0.11, p<0.001), and the same applies to the composite score of high job demands alone (effect size=0.10, p<0.001). The necessity threshold for job demands, which guarantees the absence of a particularly high level of work-related emotional exhaustion, was higher for the group of young workers with high job resources compared with young workers with low job resources.Conclusions Both psychosocial work adversities were necessary conditions for high work-related emotional exhaustion in young workers. The necessity threshold for job demands was higher for young workers with high job resources, compared with the group with low resources. This indicates that removing psychosocial work adversities and ensuring the presence of job resources might contribute to the prevention of high work-related emotional exhaustion in young workers.

This paper characterizes creative cognition as a multi-armed bandit problem involving a trade-off between exploration and exploitation in sequential decisions from experience taking place in novel uncertain environments. Creative cognition implements an efficient learning process in this kind of dynamic decision. Special emphasis is put on the optimal sequencing of divergent and convergent behavior by showing that divergence must be inhibited at one point to converge toward creative behavior so that excessive divergence is counterproductive. We test this hypothesis in two behavioral experiments, using both novel and well-known tasks and precise measures of individual differences in creative potential in middle and high school students. Results in both studies confirmed that a task-dependent mix of divergence and convergence predicted high performance in a production task and better satisfaction in a consumption task, but exclusively in novel uncertain environments. These predictions were maintained after controlling for gender, personality, incentives, and other factors. As hypothesized, creative cognition was shown to be necessary for high performance under the appropriate conditions. However, it was not necessary for getting high grades in a traditional school system.

This article delves deeper into the causal perspective of Necessary Condition Analysis (NCA). In contrast to traditional probabilistic sufficiency approaches in quantitative social science research about what will happen on average in a group of cases, NCA is interested in what will not occur in almost every case if a necessary condition is absent. Rooted in David Hume's theory of causation NCA explores factors that act as ‘must-haves’ or bottlenecks for the outcome. Operating from this necessity perspective, NCA functions deterministically (without exceptions) or non-deterministically (allowing exceptions). NCA can enrich theories and models in social science research by identifying essential factors. The article contributes to the literature by precisely describing the necessity causal perspective that is used in NCA, and by explaining how this is different from causal perspectives that are commonly used in social science research.

Necessary Condition Analysis (NCA) is a novel method that gained popularity in international business and management research in recent years. It examines cause-effect relationships in terms of necessity, where X is necessary for Y, expressed as 'if not X then not Y' in nearly all cases. This stands in contrast to conventional probabilistic causality which suggests 'if X then probably Y' in a group of cases. NCA accepts two sampling approaches: purposive sampling frequently employed in qualitative research, and probability sampling, commonly used (or assumed) in quantitative research. With dichotomous variables, purposive sampling of a small number of cases showing the outcome, can identify a necessary condition. To identify a necessary condition in a population, probability sampling and NCA's statistical test for estimating the p-value can be used. This allows conducting NCA's statistical power test to estimate the minimum required sample size for identifying a necessary condition when it exists.

This article discusses the importance of reusing existing data in research. In addition to reuse data for replication of earlier findings and for answering extended or new research questions, we propose a third application of data reuse: studying the phenomenon from an alternative causal perspective. We focus on the reuse of data with a necessity causal perspective ("if not X, then not Y") as employed in necessary condition analysis (NCA). Such reuse of data offers additional insights compared with those obtained from the conventional probabilistic causal perspective ("if X, then probably Y") as employed in regression analysis. NCA is gaining recognition in various fields, including strategic management. Reusing data for conducting NCA is an efficient way to get new causal insights. We provide recommendations on how to use NCA with existing data and emphasize the importance of transparency when reusing data.

One of the challenges of evidence-based medicine is balancing population-level findings with individualized care. Average treatment effect studies, including cohort studies and randomized controlled trials, offer insights into factors affecting disease likelihood at group level or subgroup level (precision medicine), but are limited in predicting individual outcomes. This limitation arises because average treatment effect studies operate within a probabilistic causal framework, indicating how likely a disease is when a certain individual or contextual factor is present: if X, then probably Y.

Necessary condition analysis (NCA) is an increasingly used or suggested method in many business and management disciplines including, for example, entrepreneurship, human resource management, international business, marketing, operations, public and nonprofit management, strategic management, and tourism. In the light of this development, our work delivers a review of the topics analyzed with NCA or in which NCA is proposed as a method. The review highlights the tremendous possibilities of using NCA, which hopefully encourages other researchers to try the method. To support researchers in future NCA studies, this article also provides detailed guidelines about how to best use NCA. These cover eight topics: theoretical justification, meaningful data, scatter plot, ceiling line, effect size, statistical test, bottleneck analysis, and further descriptions of NCA.

Necessary Condition Analysis (NCA) understands a cause as a necessary (but not sufficient) condition, rather than a probabilistic cause (as in regression analysis). “Necessary” means that an outcome will not occur without a certain level of the condition, independent of the rest of the causal structure (thus the condition can be a “bottleneck”, “critical factor”, “constraint”). NCA is rapidly entering a variety of research fields and can be used as a stand-alone method or in combination with other methods (e.g., multiple regression analysis, structural equation modeling, qualitative comparative analysis). This two-part seminar will introduce you to NCA and then demonstrate the use of R for conducting NCA analysis for your research. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers ECTS Equivalent points for European PhD students.

This article develops a new method for (re)analyzing data from a single disaster case to identify the temporal chain of collections of factors that was sufficient to lead to a disaster. The method combines elements of existing process methods with Mackie's (1965) interpretation of causal complexity; the INUS concept: An Insufficient but Necessary factor from a collection of factors that is Unnecessary but Sufficient for the effect. By systematically analyzing the factors that have changed shortly before the occurrence of the disaster, the method identifies not only the (collection of) factors that are sufficient for the disaster but also-by logical transformation-the collection of reversed factors that enable and ensure "normal" functioning without similar disasters and can be acted upon by management. We provide step-by-step guidelines for the graphical representation of the complexity of the disaster and the related "normal" functioning by showing the temporal relationships between collections of factors. The method may help develop an impact in two ways: first, in eliciting the factors necessary to avoid similar disasters, and second, in allowing dialogical sensemaking with practitioners at each step of the process.

Abstract Necessary condition analysis (NCA) understands cause–effect relations in terms of “necessary but not sufficient.” This means that without the right level of the cause, a certain effect cannot occur. This is independent of other causes; thus, the necessary condition can become a single bottleneck, critical factor, constraint, disqualifier, or so on that blocks the outcome when it is absent. NCA can be used as a stand-alone method or in multimethod research to complement regression-based methods such as multiple linear regression (MLR) and structural equation modeling (SEM), as well as methods like fuzzy set qualitative comparative analysis (fsQCA). The NCA method consists of four stages: formulation of necessary condition hypotheses, collection of data, analysis of data, and reporting of results. Based on existing methodological publications about NCA, guidelines for good NCA practice are summarized. These guidelines show how to conduct NCA with the NCA software and how to report the results. The guidelines support (potential) users, readers, and reviewers of NCA to become more familiar with the method and to understand how NCA should be applied, as well as how results should be reported. NCA’s rapid diffusion and broad applicability in the social, technical, and medical sciences is illustrated by the growth of the number of article publications with NCA, the diversity of disciplines where NCA is applied, and the geographical spread of researchers who apply NCA.

This article contributes to the Emerging Discourse Incubator initiative by presenting how supply chain management scholars can contribute to theory development by means of necessity theories. These are unique theories that inform what level of a concept must be present to achieve a desired level of the outcome. Necessity theories consist of concepts that are necessary but not sufficient conditions for an outcome, where the absence of a single causal concept ensures the absence of the outcome. The theoretical features of necessary conditions have important implications for understanding supply chain management phenomena and providing practical applications. In 2016, Necessary Condition Analysis (NCA) became available for building and testing necessity theories with empirical data. However, NCA has not yet been used for the development of supply chain management theories. Therefore, we explain how necessity theories can be built and tested in a supply chain management context using necessity logic and the empirical methodology of NCA. We intend to inspire scholars to develop novel necessity theories that deepen or renew our understanding of supply chain management phenomena.

This 3-day seminar on Necessary Condition Analysis (NCA) will give you the knowledge and skills you need to understand NCA and apply it in your own research. NCA is rapidly entering a variety of research fields and can be used as a stand-alone method or in combination with other methods (e.g., regression, structural equation modeling, qualitative comparative analysis - QCA). This three day seminar will present the fundamentals of NCA and how the method can be applied in professor Dul's NCA package for R. An official Instats certificate of completion is provided at the conclusion of the seminar, offering 2 ECTS Equivalent points.