Characteristics of Item Parameter Estimation for the Multidimensional Item Response Theory (MIRT)
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This study analyzes the three different estimation algorithms for recovering item parameters for the compensatory multidimensional IRT (MIRT) models. In particular, two- and four-dimensional models were investigated with different degrees of correlation between latent traits. The standards such as bias, standard error, and root mean square error were used to evaluate the recovery of item parameters for each program. The results indicated that in most conditions, Metropolis-Hasting Robbins-Monro (MH-RM) outperformed full information item factor analysis (FIIFA) and bivariate information item factor analysis (BIIFA) for a-parameters except for the independent and very low inter-trait correlation conditions where BIIFA outperformed the other algorithms. However, the MH-RM algorithm consistently produced the highest empirical standard errors compared to the other two methods for all conditions. FIIFA performed at a higher standard than BIIFA for a-parameters with moderately correlated latent traits. BIIFA is more suitable for a-parameters, especially when the levels of latent traits' independence or correlation are very low, and it is more suitable for d-parameters regardless of inter-trait correlations in the four-dimensional models. Overall, three estimation methods provided more accurate a- and d-parameter as the number of examinees increased, and less accurate a-parameter occurred as the inter-trait correlation increased. The inter-trait correlation condition did not have a dramatic impact on the recovery of d-parameter across all three algorithms.
Abstract
This study analyzes the three different estimation algorithms for recovering item parameters for the compensatory multidimensional IRT (MIRT) models. In particular, two- and four-dimensional models were investigated with different degrees of correlation between latent traits. The standards such as bias, standard error, and root mean square error were used to evaluate the recovery of item parameters for each program. The results indicated that in most conditions, Metropolis-Hasting Robbins-Monro (MH-RM) outperformed full information item factor analysis (FIIFA) and bivariate information item factor analysis (BIIFA) for a-parameters except for the independent and very low inter-trait correlation conditions where BIIFA outperformed the other algorithms. However, the MH-RM algorithm consistently produced the highest empirical standard errors compared to the other two methods for all conditions. FIIFA performed at a higher standard than BIIFA for a-parameters with moderately correlated latent traits. BIIFA is more suitable for a-parameters, especially when the levels of latent traits' independence or correlation are very low, and it is more suitable for d-parameters regardless of inter-trait correlations in the four-dimensional models. Overall, three estimation methods provided more accurate a- and d-parameter as the number of examinees increased, and less accurate a-parameter occurred as the inter-trait correlation increased. The inter-trait correlation condition did not have a dramatic impact on the recovery of d-parameter across all three algorithms.
참고문헌 (34)
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