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Michela Battauz1

  • 1Department of Economics and Statistics, University of Udine, Udine, Italy.

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Summary
This summary is machine-generated.

This study introduces a fused lasso penalty to regularize nominal response models in item response theory. This method enhances model stability and reduces overfitting for polytomous data analysis.

Keywords:
Adaptive lassocollapsefused lassoitem response theorylassomultidimensionalpolytomous responses

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Data Analysis

Background:

  • The nominal response model (NRM) is a flexible item response theory (IRT) model for polytomous data.
  • NRMs can suffer from numerical instability and overfitting due to a large number of estimated parameters.
  • Regularization techniques like LASSO are used for model selection and parameter shrinkage.

Purpose of the Study:

  • To propose a novel regularization method for NRM using a fused LASSO penalty.
  • To apply this fused LASSO penalty to both unidimensional and multidimensional NRM.
  • To evaluate the performance of the proposed method in terms of model selection and parameter estimation.

Main Methods:

  • Implementation of a fused LASSO penalty within the NRM framework.
  • Application of the method to real-world datasets.
  • Conducting simulation studies to assess performance under various conditions.

Main Results:

  • The fused LASSO penalty effectively groups response categories, leading to regularization.
  • The proposed method demonstrates good performance in real-data applications.
  • Simulation studies confirm the method's ability to mitigate overfitting and improve model stability.

Conclusions:

  • The fused LASSO penalty offers a viable approach for regularizing nominal response models.
  • This technique enhances the practical application of NRM in psychometrics and related fields.
  • The method provides a robust alternative for analyzing complex polytomous response data.