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Introduction to Necessary Condition Analysis (NCA)
For your research, if you’ve ever wanted to understand and model causal effects in a rigorous way that reflects the complexity of the real-world. NCA understands a cause as a necessary (but not sufficient) condition, rather than a probabilistic cause (as in regression analysis). “Necessary” means that a certain outcome will not occur without a certain level of the condition. This is independent of the rest of the causal structure. 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 analysis, structural equation modeling, qualitative comparative analysis).
This free 1-day seminar is being taught by Jan Dul, who invented NCA and co-authored the R package for NCA. Registration is entirely free! This seminar is perfect if, like many of us, you’ve wanted a way to go beyond simplistic ANOVA or regression-based approaches to causal effect estimation. The seminar will introduce you to NCA and then show you how to conduct analyses and make causal inferences through a series of worked examples. To register for free, use the following link — and please feel free to tell your colleagues and friends / students!