Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach

Autor/in	Klauer, Karl Christoph
Titel	Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach
Quelle	In: Psychometrika, 75 (2010) 1, S.70-98 (29 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-009-9141-0
Schlagwörter	Simulation; Bayesian Statistics; Computation; Models; Psychology; Correlation; Hypothesis Testing; Evaluation; Statistical Analysis; Item Response Theory + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Analogiemodell; Psychologie; Korrelation; Hypothesenprüfung; Hypothesentest; Evaluierung; Statistische Analyse; Item-Response-Theorie
Abstract	Multinomial processing tree models are widely used in many areas of psychology. A hierarchical extension of the model class is proposed, using a multivariate normal distribution of person-level parameters with the mean and covariance matrix to be estimated from the data. The hierarchical model allows one to take variability between persons into account and to assess parameter correlations. The model is estimated using Bayesian methods with weakly informative hyperprior distribution and a Gibbs sampler based on two steps of data augmentation. Estimation, model checks, and hypotheses tests are discussed. The new method is illustrated using a real data set, and its performance is evaluated in a simulation study. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10