2025-06-25 –, Underground, p10
Typically, researchers only hypothesize their expected effects are not null (significant), while neglecting to evaluate their actual hypotheses directly. Therefore, their tests are weakly informative and do not represent the underlying theories. As a powerful, theory-based alternative to p-values, GORICA evaluates the relative evidence for multiple hypotheses simultaneously. A researcher can, for instance, directly evaluate the hypothesis that the number of children (βNrC) is a stronger predictor for happiness than income (βInc) and age (βAge): βNrC > {βInc, βAge}. This would be impossible with p-values. Additionally, GORICA naturally ties into the goals of open science, as it requires precisely and transparently stated (and preregistered) expectations.
We present a hands-on opportunity to apply GORICA to common statistical models (like ANOVA and/or SEM, using R functions/packages lm, lavaan, or lme4) accompanied with a theoretical introduction to GORICA.
