Aichholzer Julian
Department of Methods in the Social Sciences, University of Vienna, Rathausstraße 19, 1010 Vienna, Austria.
J Res Pers. 2014 Dec;53:1-4. doi: 10.1016/j.jrp.2014.07.001.
Previous research suggests that simple structure CFAs of Big Five personality measures fail to accurately reflect the scale's complex factorial structure, whereas EFAs generally perform better. Another strand of research suggests that acquiescence or uniform response bias masks the scale's "true" factorial structure. Random Intercept EFA (RI-EFA) captures acquiescence as well as the complex item-factor structure typical for personality measures. It is applied to the NEO-FFI and the BFI scale to test whether an accurate model-to-data fit can be achieved and whether the "clarity" of the factorial structure improves. The results lend confidence in the general effectiveness of RI-EFA whenever acquiescence bias is an issue. Example M code is provided for replication.
先前的研究表明,大五人格量表的简单结构验证性因素分析(CFAs)无法准确反映该量表复杂的因子结构,而探索性因素分析(EFAs)通常表现更佳。另一项研究表明,默许或一致反应偏差掩盖了量表的“真实”因子结构。随机截距探索性因素分析(RI-EFA)既捕捉了默许现象,也捕捉了人格量表典型的复杂项目-因子结构。它被应用于修订版NEO人格量表(NEO-FFI)和大五人格量表(BFI),以检验是否能实现模型与数据的准确拟合,以及因子结构的“清晰度”是否有所提高。只要存在默许偏差问题,这些结果就为RI-EFA的总体有效性提供了信心。文中提供了示例M代码以供复制。