Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm

• URL: http://arxiv.org/abs/2502.10650v1
• Date: Sat, 15 Feb 2025 03:03:09 GMT
• Title: Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm
• Authors: Nanyu Luo, Feng Ji,
• Abstract summary: This study introduces Adversarial Variational Bayes (AVB) algorithms as an improvement to VAEs for item factor analysis. AVB incorporates an auxiliary discriminator network to reframe the estimation process as a two-player adversarial game. A further enhanced algorithm, Importance-weighted Adversarial Variational Bayes (IWAVB) is proposed and compared with Importance-weighted Autoencoders (IWAE)
• Score: 9.132370119093597
• License:
• Abstract: Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accurate parameter estimation. Variational Autoencoders (VAEs) have been one of the most impactful techniques in modeling high-dimensional latent variables in this context. However, the limited expressiveness of the inference model based on traditional VAEs can still hinder the estimation performance. This study introduces Adversarial Variational Bayes (AVB) algorithms as an improvement to VAEs for IFA with improved flexibility and accuracy. By bridging the strengths of VAEs and Generative Adversarial Networks (GANs), AVB incorporates an auxiliary discriminator network to reframe the estimation process as a two-player adversarial game and removes the restrictive assumption of standard normal distributions in the inference model. Theoretically, AVB can achieve similar or higher likelihood compared to VAEs. A further enhanced algorithm, Importance-weighted Adversarial Variational Bayes (IWAVB) is proposed and compared with Importance-weighted Autoencoders (IWAE). In an exploratory analysis of real empirical data, IWAVB demonstrated superior expressiveness by achieving a higher likelihood compared to IWAE. In confirmatory studies with simulated data, IWAVB achieved similar mean-square error results to IWAE while consistently achieving higher likelihoods. Moreover, in simulations where latent variables followed a multimodal distribution, IWAVB outperformed IWAE by providing more accurate parameter estimates. With its innovative use of GANs, IWAVB is shown to have the potential to extend IFA to handle large-scale data, facilitating the potential integration of psychometrics and multimodal data analysis.

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