Improving Research Replicability with an Easy-to-Use App for Creating and Evaluating Deterministic and Probabilistic Models of Binary Choice

Numerous deterministic and probabilistic choice models are available in academic literature. To promote cumulative science and inclusivity, enhancing the accessibility of these models for researchers, including those without formal modelling training, is essential. To this end, I will introduce an R-Shiny app specifically designed to assist researchers in translating both deterministic and probabilistic binary choice models into a unified mathematical representation. This unified representation is accomplished by deriving a minimal set of equalities and inequalities that embody the model's predicted relationship between choice probabilities. This representation enables researchers to evaluate a model's quality based on factors like logical consistency and parsimony before conducting lab studies and allocating resources. Moreover, the app offers methods to bridge the gap between deterministic and probabilistic models. It allows researchers to explore various probabilistic versions of deterministic models by incorporating psychologically meaningful sources of variability in choice probabilities. The app further simplifies the research process by automatically generating input files for in-depth model comparisons, eliminating the need for programming skills. Researchers can use these files to calculate Bayes factors and frequentist p-values in model comparisons, ensuring a thorough evaluation of competing models. Throughout the presentation, I will underscore the significance of providing user-friendly modelling tools in strengthening the replicability of research findings in the behavioral, social, and cognitive sciences.