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McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…
Descriptors: Probability, Age Differences, Children, Intervention
Khemlani, Sangeet S.; Oppenheimer, Daniel M. – Psychological Bulletin, 2011
Discounting is a phenomenon in causal reasoning in which the presence of one cause casts doubt on another. We provide a survey of the descriptive and formal models that attempt to explain the discounting process and summarize what current models do not account for and where room for improvement exists. We propose a levels-of-analysis framework…
Descriptors: Causal Models, Probability, Computation, Logical Thinking
Connelly, Brian S.; Sackett, Paul R.; Waters, Shonna D. – Personnel Psychology, 2013
Organizational and applied sciences have long struggled with improving causal inference in quasi-experiments. We introduce organizational researchers to propensity scoring, a statistical technique that has become popular in other applied sciences as a means for improving internal validity. Propensity scoring statistically models how individuals in…
Descriptors: Quasiexperimental Design, Control Groups, Inferences, Research Methodology
Holyoak, Keith J.; Lee, Hee Seung; Lu, Hongjing – Journal of Experimental Psychology: General, 2010
A fundamental issue for theories of human induction is to specify constraints on potential inferences. For inferences based on shared category membership, an analogy, and/or a relational schema, it appears that the basic goal of induction is to make accurate and goal-relevant inferences that are sensitive to uncertainty. People can use source…
Descriptors: Inferences, Logical Thinking, Bayesian Statistics, Causal Models