New Directions in Multinomial Modeling

Abstract

This special issue provides an overview of recent developments in the specification and use of multinomial processing tree (MPT) models in experimental psychology. MPT models are versatile instruments for the analysis of discrete data, and they have become increasingly popular especially among cognitive psychologists. Formally, MPT models can be regarded as a specific family of models in the more general class of parameterized multinomial or productmultinomial models (e.g., Andersen, 1990; Bishop, Fienberg, & Holland, 1975; Haberman, 1978). That is, MPT models are statistical tools for the analysis of frequency data that arise from a sample of N observations (or from k samples of N1, ... , Nk observations) which fall into a finite number of discrete categories. Moreover, they allow for the specification and statistical test of hypotheses by reducing the probability parameters of the (product-) multinomial distribution to a smaller set of basic parameters. Although MPT models share these general features with the whole class of parameterized multinomial models, they differ from most