Zhang Yifan, Chen Jinsong
The University of Hong Kong, Hong Kong.
Appl Psychol Meas. 2024 Jul;48(4-5):208-229. doi: 10.1177/01466216241261704. Epub 2024 Jun 12.
Special measurement effects including the method and testlet effects are common issues in educational and psychological measurement. They are typically covered by various bifactor models or models for the multiple traits multiple methods (MTMM) structure for continuous data and by various testlet effect models for categorical data. However, existing models have some limitations in accommodating different type of effects. With slight modification, the generalized partially confirmatory factor analysis (GPCFA) framework can flexibly accommodate special effects for continuous and categorical cases with added benefits. Various bifactor, MTMM and testlet effect models can be linked to different variants of the revised GPCFA model. Compared to existing approaches, GPCFA offers multidimensionality for both the general and effect factors (or traits) and can address local dependence, mixed-type formats, and missingness jointly. Moreover, the partially confirmatory approach allows for regularization of the loading patterns, resulting in a simpler structure in both the general and special parts. We also provide a subroutine to compute the equivalent effect size. Simulation studies and real-data examples are used to demonstrate the performance and usefulness of the proposed approach under different situations.
包括方法效应和测验题目组效应在内的特殊测量效应是教育和心理测量中的常见问题。对于连续数据，它们通常由各种双因素模型或多特质多方法（MTMM）结构模型涵盖，对于分类数据，则由各种测验题目组效应模型涵盖。然而，现有模型在适应不同类型的效应方面存在一些局限性。通过稍加修改，广义部分验证性因子分析（GPCFA）框架可以灵活地适应连续和分类情况下的特殊效应，并具有额外的优势。各种双因素、MTMM和测验题目组效应模型可以与修订后的GPCFA模型的不同变体相联系。与现有方法相比，GPCFA为一般因素和效应因素（或特质）都提供了多维性，并且可以共同解决局部依赖性、混合型格式和缺失值问题。此外，部分验证性方法允许对载荷模式进行正则化，从而在一般部分和特殊部分都产生更简单的结构。我们还提供了一个计算等效效应大小的子程序。通过模拟研究和实际数据示例来证明所提出方法在不同情况下的性能和实用性。
