de la Fuente Javier, Abad Francisco J
University of Texas at Austin.
Psicothema. 2020 Nov;32(4):590-597. doi: 10.7334/psicothema2020.96.
The inclusion of direct and reversed items in scales is a commonly-used strategy to control acquiescence bias. However, this is not enough to avoid the distortions produced by this response style in the structure of covariances and means of the scale in question. This simulation study provides evidence on the performance of two different procedures for modelling the influence of acquiescence bias on partially balanced multidimensional scales: a method based on exploratory factor analysis (EFA) with target rotation, and a method based on random intercept factor analysis (RIFA).
The independent variables analyzed in a simulation study were sample size, number of items per factor, balance of substantive loadings of direct and reversed items, size and heterogeneity of acquiescence loadings, and inter-factor correlation.
The RIFA method had better performance over most of the conditions, especially for the balanced conditions, although the variance of acquiescence factor loadings had a certain impact. In relation to the EFA method, it was severely affected by a low degree of balance.
RIFA seems the most robust approach, but EFA also remains a good alternative for medium and fully balanced scales.
在量表中纳入正向和反向条目是控制默认偏差的常用策略。然而，这不足以避免这种反应方式在所讨论量表的协方差结构和均值中产生的扭曲。本模拟研究提供了关于两种不同程序在模拟默认偏差对部分平衡多维量表影响方面表现的证据：一种基于带目标旋转的探索性因素分析（EFA）的方法，以及一种基于随机截距因素分析（RIFA）的方法。
在一项模拟研究中分析的自变量包括样本量、每个因素的条目数、正向和反向条目的实质载荷平衡、默认载荷的大小和异质性以及因素间相关性。
RIFA方法在大多数条件下表现更好，尤其是在平衡条件下，尽管默认因素载荷的方差有一定影响。相对于EFA方法，它受到低平衡度的严重影响。
RIFA似乎是最稳健的方法，但EFA对于中等和完全平衡的量表仍是一个不错的选择。
