Markov chain estimation for test theory without an answer key

DOI10.1007/BF02294733zbMath1306.62447OpenAlexW1996291436MaRDI QIDQ2259878

William H. Batchelder, George Karabatsos

Publication date: 5 March 2015

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02294733

zbMATH Keywords

Markov chain Monte CarloBayesian inferenceBayesian model selectionconsensus theoryposterior predictive model evaluation

Mathematics Subject Classification ID

Applications of statistics to psychology (62P15)

Related Items (12)

Beating the average forecast: regularization based on forecaster attributes ⋮ Cognitive psychometrics: the scientific legacy of William H. Batchelder (1940--2018) ⋮ Consensus theory for multiple latent traits and consensus groups ⋮ Cultural consensus theory: comparing different concepts of cultural truth ⋮ Consensus theory for mixed response formats ⋮ A Bayesian approach to testing decision making axioms ⋮ Hierarchical multinomial processing tree models: a latent-trait approach ⋮ Beta-MPT: multinomial processing tree models for addressing individual differences ⋮ Bayes factors: Prior sensitivity and model generalizability ⋮ Hierarchical Bayesian modeling for test theory without an answer key ⋮ Cultural consensus theory for the ordinal data case ⋮ Bayesian estimation of multinomial processing tree models with heterogeneity in participants and items

Cites Work

• Isotonic ordinal probabilistic models (ISOP)
• Markov chains for exploring posterior distributions. (With discussion)
• Test theory without an answer key
• Markov Chain Monte Carlo Convergence Diagnostics: A Comparative Review
• Practical Markov Chain Monte Carlo
• Sampling-Based Approaches to Calculating Marginal Densities
• On Measuring and Correcting the Effects of Data Mining and Model Selection
• Consensus analysis of three‐way social network data
• Bayesian Measures of Model Complexity and Fit
• Monte Carlo sampling methods using Markov chains and their applications
• Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.
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