Latent Variable Selection for Multidimensional Item Response Theory Models via $$L_{1}$$ L 1 Regularization

Jianan Sun, Yunxiao Chen, Jingchen Liu (), Zhiliang Ying and Tao Xin
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Jianan Sun: Beijing Forestry University
Yunxiao Chen: Emory University
Jingchen Liu: Columbia University
Zhiliang Ying: Columbia University
Tao Xin: Beijing Normal University

Psychometrika, 2016, vol. 81, issue 4, No 2, 939 pages

Abstract: Abstract We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an $$L_{1}$$ L 1 penalty term to the log-likelihood. The computation is carried out by the expectation–maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.

Keywords: latent variable selection; multidimensional item response theory model; $$L_{1}$$ L 1 regularization; expectation–maximization; BIC (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (10)

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DOI: 10.1007/s11336-016-9529-6

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