  With an increasing number of replication studies performed in psychological
science, the question of how to evaluate the outcome of a replication attempt
deserves careful consideration. Bayesian approaches allow to incorporate
uncertainty and prior information into the analysis of the replication attempt
by their design. The Replication Bayes Factor, introduced by Verhagen &
Wagenmakers (2014), provides quantitative, relative evidence in favor or
against a successful replication. In previous work by Verhagen & Wagenmakers
(2014) it was limited to the case of $t$-tests. In this paper, the Replication
Bayes Factor is extended to $F$-tests in multi-group, fixed-effect ANOVA
designs. Simulations and examples are presented to facilitate the understanding
and to demonstrate the usefulness of this approach. Finally, the Replication
Bayes Factor is compared to other Bayesian and frequentist approaches and
discussed in the context of replication attempts. R code to calculate
Replication Bayes factors and to reproduce the examples in the paper is
available at https://osf.io/jv39h/.
