Von König Ludwigs Hochzeit bis zum Corona-Ausfall

Our tutorial paper on openESM has been published, and we have added new datasets and features to the platform.

In a classic 1978 Memory & Cognition article, Geoff Loftus explained why noncrossover interactions are removable. These removable interactions are tied to the scale of measurement for the dependent variable and therefore do not allow unambiguous conclusions about latent psychological processes. In the present article, we present concrete examples of how this insight helps prevent experimental psychologists from drawing incorrect conclusions about the effects of forgetting and aging. In addition, we extend the Loftus classification scheme for interactions to include those on the cusp between removable and nonremovable. Finally, we use various methods (i.e., a study of citation histories, a questionnaire for psychology students and faculty members, an analysis of statistical textbooks, and a review of articles published in the 2008 issue of Psychology and Aging) to show that experimental psychologists have remained generally unaware of the concept of removable interactions. We conclude that there is more to interactions in a 2 × 2 design than meets the eye.

Conditional effects, or interaction effects, do not imply multiplicative effects. However, product terms are the default method for modeling such conditional effects in psychological research. As a result, theoretically plausible conditional effects may go undetected when the functional form is misspecified. Our study had two objectives: (1) evaluate the extent to which non-linear phenomena can be identified as spurious multiplicative (i.e., standard) interaction terms in linear models, (2) assess how well linear models capture stepwise conditional effects. In Study 1, we examined spurious interactions from non-linear main effects. We found that traditional interaction terms were associated with increased Type-I error rates and small effect sizes. Importantly, this was also the case when the predictors were uncorrelated, indicating a mechanism beyond collinearity. Additionally, we found that, if captured, the spurious interaction effects did reduce prediction error on the population level. In Study 2, we simulated genuine conditional effects, following a stepwise pattern. When effects were monotonic, product terms performed adequately, however if the conditional effect is non-monotonic a traditional interaction term in a linear model does not sufficiently capture such an effect. We conclude that relying solely on traditional interaction terms in linear models can be misleading and the failure to replicate interaction effects may partly reflect a specification crisis: Researchers default to one functional form (multiplication) while the underlying theory may dictate a different form, creating a systematic mismatch between theory and model. To validly investigate conditional effects, researchers should specify and justify the expected functional form a priori.

If you share one single thing of our team in 2026—on social media or per email with your colleagues—please let it be this machine learning competition. It was half a decade of work to get here, especi...

Interaction effects are very common in the psychological literature. However, interaction effects are typically very small and often fail to replicate. In this study, we conducted a simulation compari...