Handling missing data in variational autoencoder based item response theory.

IF 1.5 3区 心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Karel Veldkamp, Raoul Grasman, Dylan Molenaar
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引用次数: 0

Abstract

Recently Variational Autoencoders (VAEs) have been proposed as a method to estimate high dimensional Item Response Theory (IRT) models on large datasets. Although these improve the efficiency of estimation drastically compared to traditional methods, they have no natural way to deal with missing values. In this paper, we adapt three existing methods from the VAE literature to the IRT setting and propose one new method. We compare the performance of the different VAE-based methods to each other and to marginal maximum likelihood estimation for increasing levels of missing data in a simulation study for both three- and ten-dimensional IRT models. Additionally, we demonstrate the use of the VAE-based models on an existing algebra test dataset. Results confirm that VAE-based methods are a time-efficient alternative to marginal maximum likelihood, but that a larger number of importance-weighted samples are needed when the proportion of missing values is large.

在基于项目反应理论的变异自动编码器中处理缺失数据。
最近,有人提出了变异自动编码器(VAE)作为一种在大型数据集上估计高维项目反应理论(IRT)模型的方法。虽然与传统方法相比,这些方法大大提高了估算效率,但它们没有处理缺失值的自然方法。在本文中,我们将 VAE 文献中的三种现有方法应用于 IRT 设置,并提出了一种新方法。在一项针对三维和十维 IRT 模型的模拟研究中,我们比较了基于 VAE 的不同方法的性能,以及在缺失数据水平不断增加的情况下与边际最大似然估计法的性能。此外,我们还在现有的代数测试数据集上演示了基于 VAE 的模型的使用。结果证实,基于 VAE 的方法是边际最大似然法的一种省时替代方法,但当缺失值比例较大时,需要更多的重要性加权样本。
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
5.00
自引率
3.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.
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