Tu Naidan, Joo Sean, Lee Philseok, Stark Stephen
Department of Psychological Sciences, Kansas State University, Manhattan, KS, USA.
Department of Educational Psychology, University of Kansas, Lawrence, KS, USA.
Appl Psychol Meas. 2025 Sep 10:01466216251378771. doi: 10.1177/01466216251378771.
Multidimensional forced choice (MFC) formats have emerged as a promising alternative to traditional single statement Likert-type measures for assessing noncognitive traits while reducing response biases. As MFC formats become more widely used, there is a growing need for tools to support MFC analysis, which motivated the development of the package. The package estimates forced choice model parameters using Bayesian methods. It currently enables estimation of the Generalized Graded Unfolding Model (GGUM; Roberts et al., 2000)-based Multi-Unidimensional Pairwise Preference (MUPP) model using , which implements the Hamiltonian Monte Carlo (HMC) sampling algorithm. also includes functions for computing item and test information functions to evaluate the quality of MFC assessments, as well as functions for Bayesian diagnostic plotting to assist with model evaluation and convergence assessment.
多维强制选择（MFC）格式已成为一种很有前景的替代方法，可用于替代传统的单陈述李克特式测量方法，以评估非认知特质，同时减少反应偏差。随着MFC格式的使用越来越广泛，对支持MFC分析工具的需求也日益增长，这推动了该软件包的开发。该软件包使用贝叶斯方法估计强制选择模型参数。它目前能够使用实现哈密顿蒙特卡罗（HMC）采样算法的软件来估计基于广义分级展开模型（GGUM；Roberts等人，2000）的多单维成对偏好（MUPP）模型。该软件包还包括用于计算项目和测试信息函数以评估MFC评估质量的函数，以及用于贝叶斯诊断绘图以协助模型评估和收敛评估的函数。
