Not Another Post Hoc Paper: A New Look at Contrast Analysis and Planned Comparisons

Before conducting statistical analyses, scholars and researchers often have specific hypotheses about differences between groups of means. Their hypotheses are frequently tested by applying post hoc comparisons with statistically significant simple or interaction effects. With the exception of exploratory studies, using post hoc comparisons can increase Type I error rates and decrease statistical power. A well-known solution involves planning comparisons before the study. However, the coding of such planned comparisons can be difficult to understand and implement, especially for customized comparisons and interaction effects. In this tutorial, we aim to reduce such difficulties by examining all the possible types of planned comparisons, even the customized ones, for both main and interaction effects. In this tutorial, a Shiny App coded in R and called “appRiori” is presented. appRiori is coded to help in understanding both the logic behind the planned comparisons and the way to interpr...