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ISSN : 1229-067X

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Vol.34 No.2
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다차원 문항반응이론의 모수 추정 방법들의 특징들

Characteristics of Item Parameter Estimation for the Multidimensional Item Response Theory (MIRT)

한국심리학회지: 일반 / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127
2015, v.34 no.2, pp.619-640
서동기
김좌근 (미들테네시주립대학교)
김경태 (미들테네시주립대학교)
서동기, 김좌근, & 김경태. (2015). 다차원 문항반응이론의 모수 추정 방법들의 특징들. 한국심리학회지: 일반, 34(2), 619-640.
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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.

keywords
다차원, 문항반응이론, 모수추정, 요인 분석, 잠재 변수, Multidimensional, Item Response Theory, Parameter Recovery, Factor Analysis, Latent Traits

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