The paper presents a new insight of a recently proposed method named partial possibilistic regression path modeling. This method combines the principles of path modeling with those of possibilistic regression to model the net of relations among blocks of variables, where a weighted composite summarizes each block. It assumes that randomness can refer back as the measurement error, which is the error in modeling the relations between the observed variables and the corresponding composite, and the vagueness to the structural error, which is the uncertainty in modeling the relations among the composites behind each block of variables. The comparison of the proposed method with a classical composite-based path model is based on a simulation study. A case study on the use of Wikipedia in higher education illustrates a fruitful usability context of the proposed method.

本文提出了一种新的见解，最近提出了一种名为部分可能性回归路径建模的方法。该方法结合了路径建模的原理用可能回归的模型来建模变量块之间的关系网，其中加权组合总结了每个块。假定随机性可以称为测量误差，它是对观察变量与相应复合材料之间的关系进行建模时的误差，而对结构误差的模糊性则是对每种变量背后的复合材料之间的关系进行建模时的不确定性变量块。将该方法与基于经典复合路径模型的比较基于仿真研究。一项有关在高等教育中使用Wikipedia的案例研究说明了所提出方法的实用性。