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- Title
HIERARCHICAL MULTINOMIAL PROCESSING TREE MODELS: A LATENT-CLASS APPROACH.
- Authors
Klauer, Karl Christoph
- Abstract
Multinomial processing tree models are widely used in many areas of psychology. Their application relies on the assumption of parameter homogeneity, that is. on the assumption that participants do not differ in their parameter values. Tests for parameter homogeneity are proposed that can be routinely used as part of multinomial model analyses to defend the assumption. If parameter homogeneity is found to be violated, a new family of models, termed latent-class multinomial processing tree models, can be applied that accommodates parameter heterogeneity and correlated parameters, yet preserves most of the advantages of the traditional multinomial method. Estimation, goodness-of-fit tests, and tests of other hypotheses of interest are considered for the new family of models.
- Subjects
PSYCHOLOGY; GOODNESS-of-fit tests; STATISTICAL hypothesis testing; VALUES (Ethics); MATHEMATICAL statistics
- Publication
Psychometrika, 2006, Vol 71, Issue 1, p7
- ISSN
0033-3123
- Publication type
Article
- DOI
10.1007/S11336-004-1188-3