Coincidence Analysis Research Articles

Combinational regularity analysis (CORA): An introduction for psychologists.

Increasingly, psychologists make use of modern configurational comparative methods (CCMs), such as qualitative comparative analysis (QCA) and coincidence analysis (CNA), to infer regularity-theoretic causal structures from psychological data. At the same time, existing CCMs remain unable to reveal such structures in the presence of complex effects. Given the strong emphasis configurational methodology generally puts on the notion of complex causation, and the ubiquity of multieffect problems in psychological research, such as multimorbidity and polypharmacy, this limitation is severe. In this article, we introduce psychologists to combinational regularity analysis (CORA)-a new member in the family of CCMs-with which regularity-theoretic causal structures that may include complex effects can be uncovered. To this end, CORA draws on algorithms originally developed in electrical engineering for the analysis of multioutput switching circuits, which regulate the behavior of electrical signals between a set of inputs and a set of outputs. After having situated CORA within the landscape of modern CCMs, we present its technical foundations. Subsequently, we demonstrate the method's analytical and graphical capabilities by means of artificial and empirical data. To facilitate familiarization, we use the concept of the "method game" to compare CORA with QCA and CNA. Through CORA, configurational analyses of complex effects come into the analytical reach of CCMs. CORA thus represents a useful addition to the methodological toolkit of psychologists who want to analyze their data from a configurational perspective. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Data Imbalances in Coincidence Analysis: A Simulation Study

In this paper, we investigate the conditions under which data imbalances, a common data characteristic that occurs when factor values are unevenly distributed, are problematic for the performance of Coincidence Analysis (CNA). We further examine how such imbalances relate to fragmentation and noise in data. We show that even extreme data imbalances, when not combined with fragmentation or noise, do not negatively affect CNA’s performance. However, an extended series of simulation experiments on fuzzy-set data reveals that, when mixed with fragmentation or noise, data imbalances may substantially impair CNA’s performance. Furthermore, we find that the performance impairment is higher when endogenous factors are imbalanced than when exogenous factors are concerned. Our results allow us to quantify these impacts and demarcate degrees at which data imbalances should be considered as problematic. Thus, applied researchers can use our demarcation guidelines to enhance the validity of their studies.

Reducing sickness absence among public-sector healthcare employees: the difference-making roles of managerial and employee participation.

Evaluations of organizational-level interventions to prevent work-related illness have identified enabling factors, but knowledge of necessary and sufficient conditions for intervention success is needed. The aim was to identify difference-making factors that distinguish intervention groups with and without a positive intervention effect on sickness absence. An organizational-level intervention designed to decrease sickness absence by providing support from process facilitators was implemented at eight healthcare workplaces in Sweden between 2017 and 2018. We applied coincidence analysis (CNA) to analyze 34 factors and determine which factors were necessary and sufficient for a successful implementation of tailored interventional measures on an organizational level (dichotomous) and reduced sickness absence (trichotomous). Two factors perfectly explained both the presence and absence of a successful implementation: "a high sense of urgency" and "good anchoring and participation from the strategic management". The presence of either of these factors alone was sufficient for successful implementation, whereas the joint absence of both conditions was necessary and sufficient for the absence of successful implementation and an intervention effect. In addition, high employee participation was both necessary and sufficient for a high intervention effect. For organizations without high employee participation, successful implementation led to a medium-effect size. This study identified participation as a difference-maker in the implementation process. Participation from different stakeholders turned out to be important in different phases. When implementing organizational-level interventions, high participation from both strategic management and employees appears to be crucial in terms of the intervention's effect on sickness absence.

EU Member State Support to Ukraine

Abstract The EU and its member states have provided an unprecedented amount of military, humanitarian and financial aid to Ukraine after Russia's attack on February 24, 2022. However, while some EU member states have provided a disproportionately high level of support to Ukraine, other member states committed an unexpectedly low level of aid. This article aims to explain the diverging levels of EU member state support to Ukraine by building on theories of military burden-sharing. Methodologically, it applies the innovative fit robustness procedure of Coincidence Analysis (CNA). The results of the analysis indicate that conditions derived from theories of military burden-sharing can explain support to Ukraine. More specifically, the pattern of support was (mainly) explained by the level of threat that each member state faced from Russia, past military investments and public opinion. Conversely, in contrast to the frequently tested and confirmed exploitation hypothesis, the economic size of the member states did not have an impact on their support to Ukraine.

Difference-Making Pathways to Frailty Through Social Factors: A Configurational Analysis.

Social disconnection is highly prevalent in older adults and is associated with frailty. It is unclear which aspects of social disconnection are most associated with frailty, which ones are difference-making, and which combination of social factors are directly linked to frailty. We conducted a secondary coincidence analysis (CNA) of 1,071 older adults from the Rush Memory and Aging Project (mean age 79.3±7.1; 75.8% female) to identify combinations of social factors that are difference-making for frailty. We included 7 demographic (e.g., age, sex, socioeconomic status) and structural (e.g., social network), functional (e.g., social support, social activity), and quality (e.g., loneliness) aspects of social connection. An established cut score of 0.2 on a frailty index was used to define frailty as the outcome. CNA produced 46 solution models for the presence of frailty in the data set. The top-scoring model was underfit, leaving a final complex solution path for frailty with the highest fit-robustness score that met the fit parameter cutoffs. We found that the combination of loneliness, low social activity, and older age was present 82% of the time when frailty was present. The combination of loneliness, social activity, and old age is difference-making for frailty, and supports the inclusion of social factors in frailty prevention and intervention. Further research is needed in diverse data sets to better understand the interrelationships between the 3 aspects of social connection and frailty.

Applying the framework for developing and evaluating complex interventions to increase family communication about hereditary cancer.

Evaluate an intervention to increase family communication (FC) of positive hereditary cancer test results using the Framework for Developing and Evaluating Complex Interventions (FDECI). We developed 'programme theory' during the FDECI development phase by aligning intervention components with behavior change techniques (BCTs) and theoretical factors expected to improve FC. During the feasibility phase, we obtained feedback from 12 stakeholder interviews. Intervention components aligned with a total of 14 unique BCTs for which prior evidence links the BCT to theoretical factors that influence behavior change. Constructive stakeholder feedback included: more information desired, rewording to support autonomy by highlighting options, and improvements to navigation, visuals, and audio. Positive comments included: comprehensiveness of materials, modeling of conversations, and usefulness of the materials for helping a person prepare to share positive test results. The first FDECI phases were helpful for improving the intervention and planning our ongoing effectiveness and future implementation phases. Our application of the FDECI is novel, including plans to test our 'programme theory' using coincidence analysis (CNA) to determine who accesses which intervention materials, how utilizing certain materials impact the aligned theoretical factors, and whether these in turn make a difference in the behavioral outcome.

Engaging Operational Partners Is Critical for Successful Implementation of Research Products: a Coincidence Analysis of Access-Related Projects in the Veterans Affairs Healthcare System

Background/ObjectiveThe Veterans Health Administration (VHA) has prioritized timely access to care and has invested substantially in research aimed at optimizing veteran access. However, implementing research into practice remains challenging. Here, we assessed the implementation status of recent VHA access-related research projects and explored factors associated with successful implementation.DesignWe conducted a portfolio review of recent VHA-funded or supported projects (1/2015–7/2020) focused on healthcare access (“Access Portfolio”). We then identified projects with implementable research deliverables by excluding those that (1) were non-research/operational projects; (2) were only recently completed (i.e., completed on or after 1/1/2020, meaning that they were unlikely to have had time to be implemented); and (3) did not propose an implementable deliverable. An electronic survey assessed each project’s implementation status and elicited barriers/facilitators to implementing deliverables. Results were analyzed using novel Coincidence Analysis (CNA) methods.Participants/Key ResultsAmong 286 Access Portfolio projects, 36 projects led by 32 investigators across 20 VHA facilities were included. Twenty-nine respondents completed the survey for 32 projects (response rate = 88.9%). Twenty-eight percent of projects reported fully implementing project deliverables, 34% reported partially implementing deliverables, and 37% reported not implementing any deliverables (i.e., resulting tool/intervention not implemented into practice). Of 14 possible barriers/facilitators assessed in the survey, two were identified through CNA as “difference-makers” to partial or full implementation of project deliverables: (1) engagement with national VHA operational leadership; (2) support and commitment from local site operational leadership.ConclusionsThese findings empirically highlight the importance of operational leadership engagement for successful implementation of research deliverables. Efforts to strengthen communication and engagement between the research community and VHA local/national operational leaders should be expanded to ensure VHA’s investment in research leads to meaningful improvements in veterans’ care.Lay SummaryThe Veterans Health Administration (VHA) has prioritized timely access to care and has invested substantially in research aimed at optimizing veteran access. However, implementing research findings into clinical practice remains challenging, both within and outside VHA. Here, we assessed the implementation status of recent VHA access-related research projects and explored factors associated with successful implementation. Only two factors were identified as “difference-makers” to adoption of project findings into practice: (1) engagement with national VHA leadership or (2) support and commitment from local site leadership. These findings highlight the importance of leadership engagement for successful implementation of research findings. Efforts to strengthen communication and engagement between the research community and VHA local/national leaders should be expanded to ensure VHA’s investment in research leads to meaningful improvements in veterans’ care.

Qualitative Comparative Analysis: Search Target, Reflection on the Top-Down Approach, and Introduction of the Bottom-Up Approach

Based on the INUS theory of causality, the search target of qualitative comparative analysis (QCA) is to find all the minimally sufficient conditions for the outcome’s occurrence in a data set, where the condition’s sufficiency, the necessity of the condition’s components, and the completeness of the solution are three core requirements. However, QCA’s current top-down approach, which relies on a truth table and Boolean minimization, cannot meet the main objective of QCA. Conditions generated by the top-down approach can be insufficient for the outcome or contain unnecessary components that can be removed. We found evidence supporting our arguments by examining the correctness of top-down QCA in Study 1. Then, we show that QCA can also proceed with a “bottom-up” search strategy in sufficiency analysis, similar to coincidence analysis (CNA). We contrast solutions of the top-down and bottom-up QCA approaches by analyzing a simulated crisp-set data set in Study 2 and a real-world fuzzy-set data set in Study 3. Both results show that only the bottom-up approach can produce all the minimally sufficient conditions. We contribute to the ongoing debate pertain QCA solution types and QCA algorithms by critically evaluating the limitations of QCA’s top-down approach and introducing a bottom-up approach for QCA.

Configurações explicativas do desenvolvimento da resiliência nas redes de suprimentos da administração pública

Abstract Social capital is an important theoretical lens for explaining interorganizational relationships and building resilience in supply networks. This study corroborates this perspective, aiming to identify which attributes of social capital contribute to explaining the development of resilience in public administration supply networks and how they combine in this explanation. A qualitative and descriptive research was carried out, using interviews with the repertoire grid technique for data collection. Data were analyzed using Honey’s content analysis and coincidence analysis (CNA). The research results indicate that social capital proved to be fundamental to developing resilience in supply networks in public administration, through the attributes of sharing technical information, precision in communication, anticipation in communicating relevant information, reciprocity, trust, transparency, and commitment. The originality of the study lies in the use of social capital theory in studies on resilience in public administration and in the adoption of a robust data collection and analysis method that has not yet been explored in research in Brazilian public administration. The main contributions of the study were: (1) to highlight social capital as a multilevel construct that influences the development of resilience, (2) to expand studies on resilience in public administration, (3) to provide information that public managers can use to avoid or minimize the risks that jeopardize the provision of public services, and (4) to adopt an unprecedented research method in the Brazilian public administration.

Job crafting interventions: what works, for whom, why, and in which contexts? Research protocol for a systematic review with coincidence analysis

BackgroundRecent challenges in the working world that resulted from the pandemic and technological advances have underlined the importance of flexibility in how jobs are designed. Job crafting (JC) refers to self-initiated changes that employees introduce to their jobs to optimize their job design and increase the fit between the job and their needs and preferences. These behaviors can be stimulated by job crafting training interventions, which aim to change how individual employees design, organize, or manage their work. However, since the interventions are implemented in various ways, we do not know which context and intervention factors are necessary or sufficient to achieve desired outcomes. Without this knowledge, benefitting from the potential of job crafting interventions is limited. The overall aim of this project will be to investigate what combinations of context, intervention, and mechanism factors are linked with effective JC interventions. Specifically, we will detect what factors are minimally sufficient and/or necessary to produce a successful JC intervention, how they combine, as well as what are the multiple alternative paths to their success.MethodsWe will perform a systematic review of the JC interventions literature combined with coincidence analysis (CNA). We will search electronic databases of journals and utilize Rayyan software to make decisions regarding inclusion. Data regarding context (e.g., fit), intervention (e.g., types of activities), mechanisms (e.g., intention implementation), and outcomes (e.g., employee well-being, job performance) will be extracted using a pre-piloted form and coded into a crisp-set (factor present vs. absent). Analyses will be carried out using the CNA package in R.DiscussionThis review will address gaps in knowledge about the context, intervention, and mechanism-related factors that may impact the effects of JC interventions. Consequently, this review will help develop a program theory for JC interventions that explains what works, how and under which circumstances. Applying CNA to synthesize these complex solutions across multiple studies provides an innovative method that may be used in future review attempts evaluating the implementation of interventions. Finally, our synthesis will provide knowledge relevant to organizational practitioners and scholars who want to implement JC interventions.Trial registrationhttps://osf.io/2g6yx

Combinational Regularity Analysis (CORA) — a new method for uncovering complex causation in medical and health research

BackgroundModern configurational comparative methods (CCMs) of causal inference, such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have started to make inroads into medical and health research over the last decade. At the same time, these methods remain unable to process data on multi-morbidity, a situation in which at least two chronic conditions are simultaneously present. Such data require the capability to analyze complex effects. Against a background of fast-growing numbers of patients with multi-morbid diagnoses, we present a new member of the family of CCMs with which multiple conditions and their complex conjunctions can be analyzed: Combinational Regularity Analysis (CORA).MethodsThe technical heart of CORA consists of algorithms that have originally been developed in electrical engineering for the analysis of multi-output switching circuits. We have adapted these algorithms for purposes of configurational data analysis. To demonstrate CORA, we provide several example applications, both with simulated and empirical data, by means of the eponymous software package CORA. Also included in CORA is the possibility to mine configurational data and to visualize results via logic diagrams.ResultsFor simple single-condition analyses, CORA’s solution is identical with that of QCA or CNA. However, analyses of multiple conditions with CORA differ in important respects from analyses with QCA or CNA. Most importantly, CORA is presently the only configurational method able to simultaneously explain individual conditions as well as complex conjunctions of conditions.ConclusionsThrough CORA, problems of multi-morbidity in particular, and configurational analyses of complex effects in general, come into the analytical reach of CCMs. Future research aims to further broaden and enhance CORA’s capabilities for refining such analyses.

Determinants of inter-organizational implementation success: A mixed-methods evaluation of Veteran Directed Care

BackgroundVeteran Directed Care (VDC) aims to keep Veterans at risk for nursing home placement in their communities. VA medical centers (VAMCs) purchase VDC from third-party organizational providers who then partner with them during implementation. Experiences with VDC implementation have varied. Objectives: We sought to identify conditions differentiating partnerships with higher enrollment (implementation success). Methods: We conducted a case-based study with: qualitative data on implementation determinants two and eight months after program start, directed content analysis to assign numerical scores (−2 strong barrier to +2 strong facilitator), and mathematical modeling using Coincidence Analysis (CNA) to identify key determinants of implementation success. Cases consisted of VAMCs and partnering non-VAMC organizations who started VDC during 2017 or 2018. The Consolidated Framework for Implementation Research (CFIR) guided analysis. Results: Eleven individual organizations within five partnerships constituted our sample. Two CFIR determinants- Networks & Communication and External Change Agent-uniquely and consistently identified implementation success. At an inter-organizational partnership level, Networks & Communications and External Change Agent +2 (i.e., present as strong facilitators) were both necessary and sufficient. At a within-organization level, Networks & Communication +2 was necessary but not sufficient for the non-VAMC providers, whereas External Change Agent +2 was necessary and sufficient for VAMCs. Conclusion: Networks & Communication and External Change Agent played difference-making roles in inter-organizational implementation success, which differ by type of organization and level of analysis. Implications: This multi-level approach identified crucial difference-making conditions for inter-organizational implementation success when putting a program into practice requires partnerships across multiple organizations.

Defining optimal implementation packages for delivering community-wide mass drug administration for soil-transmitted helminths with high coverage

BackgroundRecent evidence suggests that community-wide mass drug administration (MDA) may interrupt the transmission of soil-transmitted helminths (STH), a group of intestinal worms that infect 1.5 billion individuals globally. Although current operational guidelines provide best practices for effective MDA delivery, they do not describe which activities are most essential for achieving high coverage or how they work together to produce effective intervention delivery. We aimed to identify the various packages of influential intervention delivery activities that result in high coverage of community-wide MDA for STH in Benin, India, and Malawi.MethodsWe applied coincidence analysis (CNA), a novel cross-case analytical method, to process mapping data as part of the implementation science research of the DeWorm3 Project, a Hybrid Type 1 cluster randomized controlled trial assessing the feasibility of interrupting the transmission of STH using bi-annual community-wide MDA in Benin, India, and Malawi. Our analysis aimed to identify any necessary and/or sufficient combinations of intervention delivery activities (i.e., implementation pathways) that resulted in high MDA coverage. Activities were related to drug supply chain, implementer training, community sensitization strategy, intervention duration, and implementation context. We used pooled implementation data from three sites and six intervention rounds, with study clusters serving as analytical cases (N = 360). Secondary analyses assessed differences in pathways across sites and over intervention rounds.ResultsAcross all three sites and six intervention rounds, efficient duration of MDA delivery (within ten days) singularly emerged as a common and fundamental component for achieving high MDA coverage when combined with other particular activities, including a conducive implementation context, early arrival of albendazole before the planned start of MDA, or a flexible community sensitization strategy. No individual activity proved sufficient by itself for producing high MDA coverage. We observed four possible overall models that could explain effective MDA delivery strategies, all which included efficient duration of MDA delivery as an integral component.ConclusionEfficient duration of MDA delivery uniquely stood out as a highly influential implementation activity for producing high coverage of community-wide MDA for STH. Effective MDA delivery can be achieved with flexible implementation strategies that include various combinations of influential intervention components.

Different algorithms, different models

This study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

Introduction: Causation, inferences, and solution types in configurational comparative methods

This special issue addresses questions of causality and validity of different solution types in configurational comparative methods (CCMs). First, what main parameters characterize the debate about correct causal interpretation of solution types? Second, to what extent has this debate been linked to a theory of causation? The special issue contribution by Mahoney and Acosta bases qualitative comparative analysis (QCA) within a regularity theory of causation integrating type-level inferences and counterfactual cases. Swiatczak clarifies how the different algorithms underlying QCA and Coincidence Analysis (CNA) produce non-identical models. Baumgartner defines and benchmarks QCA solution types against the search target of minimal robust sufficiency. Alamos-Concha et al. identify the conservative solution as most appropriate for a multimethod design combining a counterfactual causal understanding at the cross-case level with an in-depth mechanistic explanation at the within-case level. Finally, Mahoney and Owen develop a general set-theoretic framework for the study of necessity and sufficiency in quantitative research using a counterfactual understanding of causality. Our introduction reviews the state of the art, identifies current limitations and open questions regarding the theoretical basis for causal interpretation of QCA solutions.

Using Qualitative Comparative Analysis for Identifying Causal Chains in Configurational Data

In a recent contribution to Sociological Methods & Research, Baumgartner and Epple (B&E) employ Coincidence Analysis (CNA) to explain the outcome of the vote on the Swiss minaret initiative of 2009. Although the authors also present a substantive argument, their principal objective is to prove the superiority of CNA over Qualitative Comparative Analysis (QCA) due to the former’s capability of identifying causal chains in configurational data without resort to Quine–McCluskey (QMC) optimization, whereby logical contradictions are allegedly introduced into the latter’s minimization process that trivialize the results. In this methodological commentary, I demonstrate that CNA does not challenge QCA per se but merely seeks to find fault with QMC. However, the link between QCA and QMC has never been inextricable, and alternative algorithms not beset by the one-difference restriction B&E consider problematic have long been in use. Hence, it follows that CNA introduces a new algorithm but does not perforce offer a superior method. To support this argument, I showcase the untapped potential of QCA for identifying causal chains in data that even incorporate multivalent factors. In employing the eQMC algorithm, whose general approach to Boolean minimization resembles that of CNA in decisive parts, I extend the authors’ original analysis in several directions, without generating logical contradictions along the way. I conclude that future research should continue to explore the methodological implications of the issues which CNA’s introduction has raised for QCA. Ultimately, however, the integration of their individual strengths represents one of the most promising avenues for the further development of configurational comparative methods.

Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis

Abstract We present cna , a package for performing Coincidence Analysis (CNA). CNA is a config-urational comparative method for the identification of complex causal dependencies—in particular,causal chains and common cause structures—in configurational data. After a brief introduction to themethod’s theoretical background and main algorithmic ideas, we demonstrate the use of the packageby means of an artificial and a real-life data set. Moreover, we outline planned enhancements of thepackage that will further increase its applicability. Introduction Configurational comparative methods (CCMs) subsume techniques for the identification of complexcausal dependencies in configurational data using the theoretical framework of Boolean algebra andits various extensions (Rihoux and Ragin,2009). For example, Qualitative Comparative Analysis(QCA;Ragin,1987,2000,2008)—hitherto the most prominent representative of CCMs—has beenapplied in areas as diverse as business administration (e.g.,Chung,2001), environmental science(van Vliet et al.,2013), evaluation (Cragun et al.,2014), political science (Thiem,2011), public health(Longest and Thoits,2012) and sociology (Crowley,2013). Besides three stand-alone programs basedon graphical user interfaces, three R packages for QCA are currently available, each with a differentscope of functionality:

Parsimony and Causality

This paper takes issue with the current tendency in the literature on Qualitative Comparative Analysis (QCA) to settle for so-called intermediate solution formulas, in which parsimony is not maximized. I show that there is a tight conceptual connection between parsimony and causality: only maximally parsimonious solution formulas reflect causal structures. However, in order to maximize parsimony, QCA—due to its reliance on Quine-McCluskey optimization (Q-M)—is often forced to introduce untenable simplifying assumptions. The paper ends by demonstrating that there is an alternative Boolean method for causal data analysis, viz. Coincidence Analysis (CNA), that replaces Q-M by a different optimization algorithm and, thereby, succeeds in consistently maximizing parsimony without reliance on untenable assumptions.

A Coincidence Analysis of a Causal Chain

This article applies coincidence analysis ( CNA), a Boolean method of causal analysis presented in Baumgartner (2009a), to configurational data on the Swiss minaret vote of 2009. CNA is related to qualitative comparative analysis (QCA) (Ragin 2008), but contrary to the latter does not minimize sufficient and necessary conditions by means of Quine–McCluskey optimization, but based on its own custom built optimization algorithm. The latter greatly facilitates the analysis of data featuring chain-like causal dependencies among the conditions of an ultimate outcome—as can be found in the data on the Swiss minaret vote. Apart from providing a model of the causal structure behind the Swiss minaret vote, we show that a CNA of that data is preferable over a QCA.

Detecting Causal Chains in Small-n Data

The first part of this article shows that qualitative comparative analysis (QCA)—also in its most recent form as in Ragin (2008) —does not correctly analyze data generated by causal chains. The incorrect modeling of data originating from chains essentially stems from QCA’s reliance on Quine-McCluskey optimization to eliminate redundancies from sufficient and necessary conditions. Baumgartner (2009a , 2009b ) has introduced a Boolean methodology, termed coincidence analysis (CNA), which is related to QCA, yet, contrary to the latter, does not eliminate redundancies by means of Quine-McCluskey optimization. The second part of the article applies CNA to chain-generated data. It turns out that CNA successfully detects causal chains in small-[Formula: see text] data.