Yon Soo Suh

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies

Minho Lee, Yon Soo Suh

Recognizing that complex networks of skills typically exhibit hierarchical and modular organization, this article presents a Modularized Higher-Order Diagnostic Classification Model (MHO-DCM) designed to capture hierarchical relationships among attributes organized into clustered subdomains. Central to the proposed method is a representation of …

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Composite marginal likelihood estimation of higher‐order diagnostic classification models under high dimensionality

Minho Lee, Yon Soo Suh

Although full-information maximum likelihood (FIML) estimation is widely used for diagnostic classification models (DCMs), its computational efficiency deteriorates sharply in high-dimensional settings. This scalability challenge is increasingly critical as DCMs are applied to large-scale assessments, psychological testing and longitudinal studies involving many …

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Chunk-Based Higher-Order Hierarchical Diagnostic Classification Models: A Maximum Likelihood Estimation Approach

Minho Lee, Yon Soo Suh

This paper presents a class of higher-order diagnostic classification models (HO–DCMs) capable of capturing complex, nonlinear hierarchical relationships among attributes. Building on and extending prior work, we adopt a nominal response model framework in item response theory and leverage standard maximum likelihood …

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Random Item Response Data Generation Using a Limited-Information Approach: Applications to Assessing Model Complexity

Yon Soo Suh, Wes E. Bonifay, 李彩 Li Cai

Fitting propensity (FP) analysis quantifies model complexity but has been impeded in item response theory (IRT) due to the computational infeasibility of uniformly and randomly sampling multinomial item response patterns under a full-information approach. We adopt a limited-information (LI) approach, wherein we …

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Item Response Analysis of a Structured Mixture Item Response Model with mirt Package in R

Minho Lee, Yon Soo Suh, Minjeong Jeon

Structured mixture item response models (StrMixIRMs) are a special type of constrained confirmatory mixture item response theory (IRT) model for detecting latent performance differences in a measurement instrument by characteristic item groups, and classifying respondents according to these differences. In light of …

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Optimizing the Costs and GT based reliabilities of Large-scale Performance Assessments

Yon Soo Suh, Dasom Hwang, Meiling Quan, Guemin Lee

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