The goal of csemGT is to estimate the conditional
standard error of measurement (CSEM) within the Generalizability Theory
framework for the univariate, single-facet, persons-by-items (p × i)
crossed design, following Brennan (1998). It was created and is
maintained by René Gempp, paralleling
the Stata module gtcsem.
Unlike most other psychometric frameworks, Generalizability Theory
distinguishes two types of conditional measurement error: the
absolute CSEM, appropriate when decisions concern the
absolute magnitude of a person’s score (for example, mastery
classification against a fixed cutpoint), and the relative
CSEM, appropriate when decisions concern comparisons among
persons (for example, ranking or selection). csemGT
estimates both.
You can install the development version of csemGT from
GitHub with:
# Development installation
pak::pak("rgempp/csemGT")
# or
remotes::install_github("rgempp/csemGT", build_vignettes = TRUE)Once on CRAN:
install.packages("csemGT")library(csemGT)
data(iowa_like)
fit <- csem_gt(iowa_like)
print(fit)
plot(fit, plot_type = "both", error_types = "absolute")If you use csemGT in published work, please cite it
as:
Gempp, R. (2026). csemGT: Conditional Standard Error of Measurement in Generalizability Theory. R package version 1.0.0. https://github.com/rgempp/csemGT
Brennan, R. L. (1998). Raw-score conditional standard errors of measurement in generalizability theory. Applied Psychological Measurement, 22(4), 307–331. https://doi.org/10.1177/014662169802200401