University of Alabama, 309E LB Gorgas Library, Tuscaloosa, AL, 35487, USA.
University of Wyoming, Laramie, WY, USA.
Behav Res Methods. 2019 Apr;51(2):651-662. doi: 10.3758/s13428-018-1069-9.
The Bayesian literature has shown that the Hamiltonian Monte Carlo (HMC) algorithm is powerful and efficient for statistical model estimation, especially for complicated models. Stan, a software program built upon HMC, has been introduced as a means of psychometric modeling estimation. However, there are no systemic guidelines for implementing Stan with the log-linear cognitive diagnosis model (LCDM), which is the saturated version of many cognitive diagnostic model (CDM) variants. This article bridges the gap between Stan application and Bayesian LCDM estimation: Both the modeling procedures and Stan code are demonstrated in detail, such that this strategy can be extended to other CDMs straightforwardly.
贝叶斯文献表明， Hamiltonian Monte Carlo（HMC）算法对于统计模型估计非常强大和高效，特别是对于复杂模型。Stan 是一个基于 HMC 的软件程序，被引入作为心理测量建模估计的一种手段。然而，对于使用 HMC 实现对数线性认知诊断模型（LCDM），即许多认知诊断模型（CDM）变体的饱和版本，并没有系统的指导方针。本文弥合了 Stan 应用与贝叶斯 LCDM 估计之间的差距：详细演示了建模过程和 Stan 代码，以便可以将此策略直接扩展到其他 CDM。
