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Lucy D'Agostino McGowan; Travis Gerke; Malcolm Barrett – Journal of Statistics and Data Science Education, 2024
This article introduces a collection of four datasets, similar to Anscombe's quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the…
Descriptors: Statistics Education, Programming Languages, Statistical Inference, Causal Models
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Sözeri, Mahmut Can; Kert, Serhat Bahadir – International Journal of Computer Science Education in Schools, 2021
In this study, the effects of interactive video usage in programming education on academic achievement and self-efficacy perception of programming were investigated by taking into account learning styles. The research was patterned according to the causal-comparative model, and also, correlation analysis was performed for related research.…
Descriptors: Correlation, Interactive Video, Programming, Academic Achievement
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Perales, Jose C.; Shanks, David R. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2008
It has been proposed that causal power (defined as the probability with which a candidate cause would produce an effect in the absence of any other background causes) can be intuitively computed from cause-effect covariation information. Estimation of power is assumed to require a special type of counterfactual probe question, worded to remove…
Descriptors: Figurative Language, Probability, Cognitive Mapping, Knowledge Representation