This project hosts online supplementary materials for the **open-access** publication: - Glas, C. A. W., Jorgensen, T. D., & ten Hove, D. (in press). Reducing attenuation bias in regression analyses involving rating scale data via psychometric modeling. *Psychometrika*. https://doi.org/10.1007/s11336-024-09967-4 The supplemental materials include the following files, organized into corresponding folders: - (Open/Win)BUGS model syntax - R syntax to run the models in JAGS The PDF file "Legenda of zipped files" contains details about the contents of each zipped folder (corresponding to example analyses in the paper). ### Abstract Many studies in fields such as psychology and educational sciences obtain information about attributes of subjects through observational studies, in which raters score subjects using multiple-item rating scales. Error variance due to measurement effects, such as items and raters, attenuate the regression coefficients and lower the power of (hierarchical) linear models. A modeling procedure is discussed to reduce the attenuation. The procedure consists of (1) an item response theory (IRT) model to map the discrete item responses to a continuous latent scale and (2) a generalizability theory (GT) model to separate the variance in the latent measurement into variance components of interest and nuisance variance components. It will be shown how measurements obtained from this mixture of IRT and GT models can be embedded in (hierarchical) linear models, both as predictor or criterion variables, such that error variance due to nuisance effects are partialled out. Using examples from the field of educational measurement, it is shown how general-purpose software can be used to implement the modeling procedure.