17.03.2022 New preprint: Tutorial on Multinomial Processing Tree Modeling

Tutorial on Multinominal Processing Tree Modeling: How to Develop, Test, and Extend MPT Models

Authors: Oliver Schmidt, Edgar Erdfelder & Daniel W. Heck

Many psychological theories assume that observable responses are determined by multiple latent processes. Multinomial processing tree (MPT) models are a class of cognitive models for discrete responses that allow researchers to disentangle and measure such processes. Before applying MPT models to specific psychological theories, it is necessary to tailor a model to specific experimental designs. In this tutorial, we explain how to develop, fit, and test MPT models using the classical pair-clustering model as a running example. The first part covers the required data structures, model equations, identifiability, model validation, maximum-likelihood estimation, hypothesis tests, and power analyses using the software multiTree. The second part introduces hierarchical MPT modeling which allows researchers to account for individual differences and to estimate the correlations of latent processes among each other and with additional covariates using the TreeBUGS package in R. All examples including data and annotated analysis scripts are provided at the Open Science Framework (https://osf.io/24pbm/).

Access the preprint (PsyArXiv):