There is a need for replication studies in psychology, yet resources are scarce. Study selection strategies are required that can guide researchers in which studies to prioritise for replication....Show moreThere is a need for replication studies in psychology, yet resources are scarce. Study selection strategies are required that can guide researchers in which studies to prioritise for replication. The goal of this paper was to examine potential selection strategies and to identify possible issues with these strategies. Therefore a quantitative method for Replication Value (RV), inspired by Isager (2019), was proposed. RV determines the relative importance of replicating a study and was defined as impact over uncertainty. The studies in this paper formulated and compared different operationalizations of RV. Web of Science (WoS) was used to extract relevant data on a random sample of papers from WoS’s social psychology category. The first study examined a RV formula using minimal information, with yearly citations as a measure for impact and sample size as a measure for uncertainty. Study 1 also introduced Statcheck as a method to examine potential relations between RV-ranking and erroneous reporting. Study 2 elaborated on study 1, combining p-values with sample size as a measure for uncertainty. As part of this study, p-curve analysis was conducted to find relations between evidential value and paper ranking. Study 3 elaborated further, adding Altmetric score, a measure for societal influence of a paper, as a measure for impact. For all studies, similarity between RV-rankings was examined using Rank-Biased Overlap (RBO). Results tentatively indicate that sample size and citations are measures that can be useful when creating RV-formulas. Adding p-values to the RV-equation wasn’t beneficial, because it hardly changed the ranking of higher ranking papers. The addition of Altmetric score did change the RV-ranking and might be of interest to researchers interested in emphasising societal impact. Overall, this paper lays a groundwork for future RV research, mainly by exploring possible metrics involved in RV equations, but also by pointing out potential issues when using RV equations.Show less