Replication Data for: Visual Heuristics for Marginal Effects Plots
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资源简介：
Common visual heuristics used to interpret marginal effects plots are susceptible to Type-1 error. This susceptibility varies as a function of (1) sample size, (2) stochastic error in the true data generating process, and (3) the relative size of the main effects of the causal variable versus the moderator. I discuss simple alternatives to these standard visual heuristics that may improve inference and do not depend on regression parameters.
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Cornell University创建时间：
2019-01-01
