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Scientific theories reflect some of humanity's greatest epistemic achievements. The best theories motivate us to search for discoveries, guide us towards successful interventions, and help us to explain and organize knowledge. Such theories require a high degree of specificity, and specifying them requires modeling skills. Unfortunately, in psychological science, theories are often not precise, and psychological scientists often lack the technical skills to formally specify existing theories. This problem raises the question: How can we promote formal theory development in psychology, where there are many content experts but few modelers? In this paper, we discuss one strategy for addressing this issue: a Many Modelers approach. Many Modelers consist of mixed teams of modelers and non-modelers that collaborate to create a formal theory of a phenomenon. We report a proof of concept of this approach, which we piloted as a three-hour hackathon at the SIPS 2021 conference. We find that (a) psychologists who have never developed a formal model can become excited about formal modeling and theorizing; (b) a division of labor in formal theorizing could be possible where only one or a few team members possess the prerequisite modeling expertise; and (c) first working prototypes of a theoretical model can be created in a short period of time.

Research in social sciences has been questioned in the past decade due to the failure to replicate earlier work. To improve the trust in results, best practices that reduce data‐analytical flexibility have been proposed, such as preregistration. Another more recent such development regards the multiverse approach. The current study sought to demonstrate the benefits of applying the multiverse approach within developmental psychology. Using the relation between self‐esteem and peer victimization in adolescence as an example, we examined how both the concurrent as well as longitudinal association was impacted by data‐analytical decisions, such as the operationalization of variables, treatment of outliers, inclusion of covariates, and paths estimated in the model. Indeed, through the multiverse approach, we showed that and how these decisions matter for the model fit and estimates of the associations. We were thus able to illustrate that the multiverse approach increases transparency, makes it possible to examine the robustness of effects and can help advance theory building. Limitations of this approach as well as recommendations for other researchers are discussed.

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We expect that consensus meetings, where researchers come together to discuss their theoretical viewpoints, prioritize the factors they agree are important to study, standardize their measures, and determine a smallest effect size of interest, will prove to be a more efficient solution to the lack of coordination and integration of claims in science than integrative experiments.