%0 Journal Article
%T Visual heuristics for marginal effects plots
%A Thomas B. Pepinsky
%J Research & Politics
%@ 2053-1680
%D 2018
%R 10.1177/2053168018756668
%X Common visual heuristics used to interpret marginal effects plots are susceptible to Type-1 error. This susceptibility varies as a function of (a) sample size, (b) stochastic error in the true data generating process, and (c) 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
%K Interaction terms
%K marginal effects plots
%K conditional hypotheses
%K data visualization
%U https://journals.sagepub.com/doi/full/10.1177/2053168018756668