Comparing Methods for Modeling Acquiescence in Multidimensional Partially Balanced Scales.

Background: 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).

Method: 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.

Results: 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.

Conclusions: RIFA seems the most robust approach, but EFA also remains a good alternative for medium and fully balanced scales.