Correction to "Worse than measurement error: Consequences of inappropriate latent variable measurement models" by Rhemtulla et al. (2020).

Abstract

Reports an error in "Worse than measurement error: Consequences of inappropriate latent variable measurement models" by Mijke Rhemtulla, Riet van Bork and Denny Borsboom (Psychological Methods, 2020[Feb], Vol 25[1], 30-45, http://dx.doi.org/10.1037/met0000220; see record 2019-31247-001). In the article, an error in code produced incorrect results depicted in Figure 10, in the associated text, and in the parameter sweep results that were presented in the supplemental materials. The results shown in Figure 10 have been corrected in the online article. The code and supplemental materials have been updated (both the original and updated versions are available on the project OSF page located at https://osf.io/f8g4d). Corrections to the text are shown. The authors thank Leonhard Volz for identifying the error in the posted code. (The following abstract of the original article appeared in record 2019-31247-001.) Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model. We explore what happens when data that are not generated from a common factor model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical reanalysis, we show that when a common factor model is misused, structural parameter estimates that indicate the relations among psychological constructs can be severely biased. Moreover, this bias can arise even when model fit is perfect. In some situations, composite models perform better than common factor models. These demonstrations point to a need for models to be justified on substantive, theoretical bases in addition to statistical ones. (PsycInfo Database Record (c) 2026 APA, all rights reserved).