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

Jianan Sun¹, Ziwen Ye¹

¹Department of Mathematics, College of Science, Beijing Forestry University, Beijing, China.

Frontiers in Psychology

|September 24, 2019

Summary

This study introduces a novel pattern recognition method using LASSO for identifying item-trait patterns in multidimensional computerized adaptive testing (MCAT). The method accurately identifies patterns in replenished items, crucial for effective test construction.

Keywords:

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

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Area of Science:

Psychometrics
Educational Measurement
Statistical Modeling

Background:

Multidimensional computerized adaptive testing (MCAT) is a key area in psychometrics.
Identifying item-trait patterns for replenished items is crucial for maintaining test integrity and accuracy in MCAT.

Purpose of the Study:

To propose and evaluate a novel pattern recognition method for detecting item-trait patterns of replenished items in MCAT.
To assess the accuracy and efficiency of the proposed method under various psychometric conditions.

Main Methods:

A pattern recognition method based on the least absolute shrinkage and selection operator (LASSO) was developed.
Simulation studies were conducted to evaluate the method's performance across different latent trait correlations, item discrimination levels, test lengths, and item selection criteria.

Main Results:

The proposed LASSO-based method demonstrated high accuracy and efficiency in identifying item-trait patterns for replenished items.
Successful pattern recognition was observed in both two-dimensional and three-dimensional item pools.

Conclusions:

The LASSO-based pattern recognition method is a viable and effective tool for identifying item-trait patterns in MCAT.
This approach contributes to the advancement of item replenishment strategies in multidimensional adaptive testing.