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• W2959642935 abstract "Social scientists using statistical models and more qualitative techniques frequently employ divergent approaches to thinking about causality. Statistical methodologies tend to draw on probabilistic understandings of causality. Qualitative research traditions, however, have advanced a sophisticated framework around necessary and sufficient conditions. In particular, the qualitative comparative analysis approach has embraced theory development that emphasizes equifinality and complex causal relationships. This article reviews the two traditions and explores how a causal framework grounded in necessary and sufficient conditions can be adapted to statistical models. A logistic regression analysis of major contributions to peacekeeping missions is used to illustrate both the viability of blending the two traditions as well as the potential for more sophisticated theory development and testing." @default.
• W2959642935 created "2019-07-23" @default.
• W2959642935 creator A5029006156 @default.
• W2959642935 date "2019-07-13" @default.
• W2959642935 modified "2023-10-10" @default.
• W2959642935 title "Complexity, Causality, and Control in Statistical Modeling" @default.
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• W2959642935 doi "https://doi.org/10.1177/0002764219859641" @default.
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