HMMTree: a computer program for latent-class hierarchical multinomial processing tree models.
Latent-class hierarchical multinomial models are an important extension of the widely used family of multinomial processing tree models, in that they allow for testing the parameter homogeneity assumption and provide a framework for modeling parameter heterogeneity. In this article, the computer program HMMTree is introduced as a means of implementing latent-class hierarchical multinomial processing tree models. HMMTree computes parameter estimates, confidence intervals, and goodness-of-fit statistics for such models, as well as the Fisher information, expected category means and variances, and posterior probabilities for class membership. A brief guide to using the program is provided.
Stahl
Christoph
C
Institut für Psychologie, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany. stahl@psychologie.uni-freiburg.de
Klauer
Karl Christoph
KC
D000465:Algorithms; D003627:Data Interpretation, Statistical; D008960:Models, Psychological; D015233:Models, Statistical; D011589:Psychology, Experimental / Q000379:methods*; D012984:Software