The Improved EMS Algorithm for Latent Variable Selection in M3PL Model.

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Laixu Shang, Ping-Feng Xu, Na Shan, Man-Lai Tang, Qian-Zhen Zheng
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引用次数: 0

Abstract

One of the main concerns in multidimensional item response theory (MIRT) is to detect the relationship between items and latent traits, which can be treated as a latent variable selection problem. An attractive method for latent variable selection in multidimensional 2-parameter logistic (M2PL) model is to minimize the observed Bayesian information criterion (BIC) by the expectation model selection (EMS) algorithm. The EMS algorithm extends the EM algorithm and allows the updates of the model (e.g., the loading structure in MIRT) in the iterations along with the parameters under the model. As an extension of the M2PL model, the multidimensional 3-parameter logistic (M3PL) model introduces an additional guessing parameter which makes the latent variable selection more challenging. In this paper, a well-designed EMS algorithm, named improved EMS (IEMS), is proposed to accurately and efficiently detect the underlying true loading structure in the M3PL model, which also works for the M2PL model. In simulation studies, we compare the IEMS algorithm with several state-of-art methods and the IEMS is of competitiveness in terms of model recovery, estimation precision, and computational efficiency. The IEMS algorithm is illustrated by its application to two real data sets.

用于 M3PL 模型中潜在变量选择的改进 EMS 算法。
多维项目反应理论(MIRT)的主要关注点之一是检测项目与潜在特质之间的关系,这可以看作是一个潜在变量选择问题。在多维双参数逻辑(M2PL)模型中,一种有吸引力的潜变量选择方法是通过期望模型选择(EMS)算法使观察到的贝叶斯信息准则(BIC)最小化。EMS 算法扩展了 EM 算法,允许在迭代中更新模型(如 MIRT 中的负载结构)和模型下的参数。作为 M2PL 模型的扩展,多维三参数逻辑(M3PL)模型引入了一个额外的猜测参数,这使得潜变量选择更具挑战性。本文提出了一种精心设计的 EMS 算法,名为改进 EMS(IEMS),用于准确有效地检测 M3PL 模型中潜在的真实负载结构,该算法同样适用于 M2PL 模型。在模拟研究中,我们将 IEMS 算法与几种最先进的方法进行了比较,IEMS 在模型恢复、估计精度和计算效率方面都具有竞争力。IEMS 算法在两个真实数据集上的应用说明了这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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