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