The asymptotic posterior normality of the latent trait for polytomous IRT models

DOI10.1007/BF02294549zbMath0871.62097OpenAlexW2023104818MaRDI QIDQ676469

Hua-Hua Chang

Publication date: 6 October 1997

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02294549

zbMATH Keywords

consistencyitem response theoryefficiencyFisher informationKullback-Leibler informationasymptotic posterior normalitypolytomous IRT modelsexaminee responsesglobal information functioninformation indexitem category response functionordered categories

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (9)

What can we learn from plausible values? ⋮ Kullback-Leibler information and its applications in multi-dimensional adaptive testing ⋮ Estimation of generalized linear latent variable models via fully exponential Laplace approximation ⋮ Latent variable models for ordinal data by using the adaptive quadrature approximation ⋮ The Role of Posterior Densities in Latent Variable Models for Ordinal Data ⋮ Log-multiplicative association models as item response models ⋮ Second-order probability matching priors for the person parameter in unidimensional IRT models ⋮ Characterizing the manifest probability distributions of three latent trait models for accuracy and response time ⋮ Asymptotic posterior normality of multivariate latent traits in an IRT model

Cites Work

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• A taxonomy of item response models
• A rating formulation for ordered response categories
• A Rasch model for partial credit scoring
• On the sampling theory foundations of item response theory models
• Essential independence and likelihood-based ability estimation for polytomous items
• The asymptotic posterior normality of the latent trait in an IRT model
• Estimating item parameters and latent ability when responses are scored in two or more nominal categories
• Approximation Theorems of Mathematical Statistics
• Note on the Consistency of the Maximum Likelihood Estimate

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