JASPROC

The goal of this project was to transform the way in which researchers and educators use conditional process models in the social sciences. Conditional process models are a family of statistical models that feature moderation, mediation, or both, in various configurations. Highly popular throughout the social sciences, the current state of its main
implementation has always been below par; specifically, the main implementation of conditional process is in SPSS (a commercial, closed-source program called PROCESS) and is presented as a catalogue of 57 models (a substandard educational approach). In this project, conditional process models were implemented in the open-source statistical software program JASP. In JASP, conditional process models can be constructed either using the catalogue approach or by manual construction. The JASP graphical user interface provides an immediate graph of the associated model, allowing for efficient adjustment and learning. The JASP Process module also implements several important statistical safeguards, presents both a Bayesian and a frequentist version, and can generally be considered the best software implementation of this style of models. Consequently, we expect the JASP Process module to become the tool of choice for teaching and research. A blog post that showcases the functionality of the Process module is available at https://jasp-stats.org/2024/01/29/causal-inference-in-jasp-the-process-module/. With the software firmly in place, the next step is to develop educational materials to make it easier for teachers to integrate the JASP module within curriculum materials.