A LASSO-Based Method for Detecting Item-Trait Patterns of Replenished Items in Multidimensional Computerized Adaptive Testing

Abstract

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Multidimensional computerized adaptive testing (MCAT) is one of the widely discussed topics in psychometrics. Within the context of item replenishment in MCAT, it is important to identify the item-trait pattern for each replenished item, which indicates the set of the latent traits that are measured by each replenished item in the item pool. We propose a pattern recognition method based on the least absolute shrinkage and selection operator (LASSO) to detect the optimal item-trait patterns of the replenished items via an MCAT test. Simulation studies are conducted to investigate the performance of the proposed method in pattern recognition accuracy under different conditions across various latent trait correlation, item discrimination, test lengths, and item selection criteria in the test. Results show that the proposed method can accurately and efficiently identify the item-trait patterns of the replenished items in both the two-dimensional and three-dimensional item pools.

Keywords

• multidimensional computerized adaptive testing
• multidimensional two parameter logistic model
• replenished items
• item-trait pattern recognition
• variable selection
• least absolute shrinkage and selection operator