Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis

Baumgärtner, Michael; Thiem, Alrik

doi:10.32614/rj-2015-014

Cited by 45 publications

(53 citation statements)

References 27 publications

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“…• span: Numerical vector with two elements (span [1]: starting point of the data set, span [2]: end point of data set).…”

Section: Cces2ts()mentioning

confidence: 99%

“…This paper introduces CoinCalc, an easy-to-handle implementation of ECA in the open statistical software R, which is available via the Comprehensive R Archive Network (CRAN, www.r-project.org). We emphasize that the CRAN repository already contains the package CNA for performing an entirely different type of analysis referred to as coincidence analysis [1], and that the same term is also used in particle physics [21] in yet another different context. Within the framework of CoinCalc, we exclusively refer to the definition of event coincidence analysis as com-prehensively described by Donges et al [8].…”

Section: Introductionmentioning

confidence: 99%

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CoinCalc —A new R package for quantifying simultaneities of event series

Siegmund

Donner

2017

Computers & Geosciences

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We present the new R package CoinCalc for performing event coincidence analysis (ECA), a novel statistical method to quantify the simultaneity of events contained in two series of observations, either as simultaneous or lagged coincidences within a user-specific temporal tolerance window. The package also provides different analytical as well as surrogatebased significance tests (valid under different assumptions about the nature of the observed event series) as well as an intuitive visualization of the identified coincidences. We demonstrate the usage of CoinCalc based on two typical geoscientific example problems addressing the relationship between meteorological extremes and plant phenology as well as that between soil properties and land cover.

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“…• span: Numerical vector with two elements (span [1]: starting point of the data set, span [2]: end point of data set).…”

Section: Cces2ts()mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CoinCalc —A new R package for quantifying simultaneities of event series

Siegmund

Donner

2017

Computers & Geosciences

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“…The configurational approach is a mathematical approach to identify combinations of conditions necessary or sufficient to achieve a specific, desired outcome (e.g., reduction in hospitalizations) ( Baumgartner & Epple, 2014 ; Baumgartner & Thiem, 2015 ; Cragun et al, 2016 ; Kane, Lewis, Williams, & Kahwati, 2014 ). The configurational approach is fundamentally different than traditional correlation- and regression-based methods in at least five ways.…”

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confidence: 99%

Identifying the Implementation Conditions Associated With Positive Outcomes in a Successful Nursing Facility Demonstration Project

et al. 2020

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Background and Objectives To identify the implementation barriers, facilitators, and conditions associated with successful outcomes from a clinical demonstration project to reduce potentially avoidable hospitalizations of long-stay nursing facility residents in 19 Indiana nursing homes. Research Design and Methods Optimizing Patient Transfers, Impacting Medical quality, Improving Symptoms—Transforming Institutional Care (OPTIMISTIC) is a multicomponent intervention that includes enhanced geriatric care, transition support, and palliative care. The configurational analysis was used to analyze descriptive and quantitative data collected during the project. The primary outcome was reductions in hospitalizations per 1,000 eligible resident days. Results Analysis of barriers, facilitators, and conditions for success yielded a model with 2 solution pathways associated with a 10% reduction in potentially avoidable hospitalizations per 1,000 resident days: (a) lower baseline hospitalization rates and investment of senior management; or (b) turnover by the director of nursing during the observation period. Conditions for success were similar for a 20% reduction, with the addition of increased resident acuity. Discussion and Implications Key conditions for successful implementation of the OPTIMISTIC intervention include strong investment by senior leadership and an environment in which baseline hospitalization rates leave ample room for improvement. Turnover in the position of director of nursing also linked to successful implementation; this switch in leadership may represent an opportunity for culture change by bringing in new perspectives and viewpoints. These findings help define the conditions for the successful implementation of the OPTIMISTIC model and have implications for the successful implementation of interventions in the nursing facility more generally.

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“…In the short run, generalizations to multi-value QCA (Cronqvist and Berg-Schlosser 2009;Thiem 2013) and further research into the issue of data loss under the parsimonious solution type appear to be the two most promising avenues. Sensitivity evaluations of methods closely related to QCA, such as Coincidence Analysis, provide another possibility as corresponding software has now become available (Ambuehl et al 2015;Baumgartner and Thiem 2015a). Last but not least, Monte Carlo simulations should be examined as a third alternative to exhaustive enumeration and combinatorial computation.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Sensitivity Diagnostics for Qualitative Comparative Analysis: A Combinatorial Approach

2016

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Sensitivity diagnostics has recently been put high on the agenda of methodological research into Qualitative Comparative Analysis (QCA). Existing studies in this area rely on the technique of exhaustive enumeration, and the majority of works examine the reactivity of QCA either only to alterations in discretionary parameter values or only to data quality. In this article, we introduce the technique of combinatorial computation for evaluating the interaction effects between two problems afflicting data quality and two discretionary parameters on the stability of QCA reference solutions. In this connection, we challenge a hitherto unstated assumption intrinsic to exhaustive enumeration, show that combinatorial computation permits the formulation of general laws of sensitivity in QCA, and demonstrate that our technique is most efficient.

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Identifying Complex Causal Dependencies in Configurational Data with Coincidence Analysis

Cited by 45 publications

References 27 publications

CoinCalc —A new R package for quantifying simultaneities of event series

CoinCalc —A new R package for quantifying simultaneities of event series

Identifying the Implementation Conditions Associated With Positive Outcomes in a Successful Nursing Facility Demonstration Project

Enhancing Sensitivity Diagnostics for Qualitative Comparative Analysis: A Combinatorial Approach

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