The aim of the article is to propose a Bayesian estimation through Markov chain Monte Carlo of a multidimensional item response theory model for graded responses with an additive structure with correlated latent traits. A simulation study is conducted to evaluate the model parameter recovery under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameters are well reproduced when the sample size is sufficiently large (n = 1, 000), while the worst recovery is observed for small sample size (n = 500), and four response categories with a short number of test items.

Bayesian estimation of a multidimensional additive graded response model for correlated traits

MARTELLI, IRENE
;
MATTEUCCI, MARIAGIULIA;MIGNANI, STEFANIA
2016

Abstract

The aim of the article is to propose a Bayesian estimation through Markov chain Monte Carlo of a multidimensional item response theory model for graded responses with an additive structure with correlated latent traits. A simulation study is conducted to evaluate the model parameter recovery under different conditions (sample size, test and subtest length, number of response categories, and correlation structure). The results show that the parameters are well reproduced when the sample size is sufficiently large (n = 1, 000), while the worst recovery is observed for small sample size (n = 500), and four response categories with a short number of test items.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/315115
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