다차원 명명반응 모형을 위한 척도연계 방법

초록

In item response theory (IRT), the exploratory multidimensional nominal response (NR) model is one of the most general models, from which several dichotomous and polytomous models can be derived. The purpose of this paper is to present common-item scale-linking methods for the multidimensional NR model and investigate their characteristics and performances. For this, the least squares (LSQ) method and the item-category response function (IRF) method were presented and their functions were examined through computer simulations. The simulation study included three factors of (a) type of population ability distribution, (b) sample size, and (c) number of anchor items. Simulation results showed that overall, the LSQ method was inferior to the IRF method in estimating the linking coefficients (rotation matrix and translation vector) used to transform an arbitrary multidimensional ability scale to the base scale. In particular, the LSQ method tended to considerably under-estimate or over-estimate the variances of ability variables when recovering the underlying ability distribution through scale linking. On the other hand, the IRF method recovered well the parameters of the underlying ability distribution as well as the item parameters through scale linking. At the end of the paper, a discussion is provided as to how the IRF linking method should be implemented in association with the process of test data analysis based on the multidimensional NR model.

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