Compute and compare conditional
standard errors of measurement (CSEM) across score distributions
using methods from classical test theory. Includes approaches for
smoothing, bootstrapped CSEM, standardized CSEM, CSEM for scale scores,
and assessment of properties of split-half scores. Also supports
comparison with global standard errors derived from reliability
coefficients and graphical visualization of CSEM curves and
relative precision across observed score ranges. Some of these implemented
methods are based on work by
Lord (1955)
The CSEM package provides functions to compute various methods of Conditional Standard Error of Measurement (CSEM), including:
Additionally, the package includes tools for: - Splitting test items into halves (check.split) - Computing reliability estimates (check.alpha, check.angoff) - Testing the distribution of split scores (check.distribution) - Visualizing results (check.plot)
To install and load the package, run:
```r # Install from GitHub (if applicable) devtools::install_github(“cmerinos/CSEM”)
library(CSEM)
set.seed(123) dhalf1 <- matrix(sample(1:5, 30, replace = TRUE), nrow = 10) dhalf2 <- matrix(sample(1:5, 30, replace = TRUE), nrow = 10)
csemMF(half1 = dhalf1, half2 = dhalf2, data = dataset, reliability.coef = 0.9, n.items = 6, min.score.item = 0, max.score.item = 4, conf.level = .95)
csemthorndike(half1 = dhalf1, half2 = dhalf2, n.groups = 4)
check.distribution(half1 = dhalf1, half2 = dhalf2, B = 2000, conf = .95)
?CSEM help(package = “CSEM”)
Author: Cesar Merino-Soto
Maintainer: Cesar Merino-Soto [email protected]{.email}
License: GPL-3
Of course, contributions always are welcome! If you find an issue, please report it or submit a pull request.