Multidimensional item response theory (MIRT) models have generated increasing interest in the psychometrics literature. Efficient approaches for estimating MIRT models with dichotomous responses have been developed, but constructing an equally efficient and robust algorithm for polytomous models has received limited attention. To address this gap, this paper presents a novel Gaussian variational estimation algorithm for the multidimensional generalized partial credit model (MGPCM). The proposed algorithm demonstrates both fast and accurate performance, as illustrated through a series of simulation studies and two real data analyses.

翻译：多维项目反应理论（MIRT）模型在心理测量学文献中引起了越来越多的关注。针对二分响应数据的MIRT模型已经发展了高效的估计方法，但构建同样高效且稳健的多分响应模型算法却鲜有关注。为填补这一空白，本文提出了一种针对多维广义部分评分模型（MGPCM）的高斯变分估计算法。通过一系列模拟研究与两项实证数据分析表明，该算法兼具快速与准确的性能表现。