cog_cat: Administer Cognitive Tests Using Computerized Adaptive...

In cogirt: Cognitive Testing Using Item Response Theory

Administer Cognitive Tests Using Computerized Adaptive Testing

Description

This function accepts an RDA file or a list containing selected objects and returns omega estimates, the standard error of omega, and the optimal next condition to administer for single-subject computerized adaptive testing. Adaptive testing is guided by D-optimality (see Segall, 2009).

Usage

cog_cat(rda = NULL, obj_fun = NULL, int_par = NULL)

Arguments

rda	An RDA file (or list) containing y, kappa, gamma, lambda, condition, omega_mu, omega_sigma2, zeta_mu, zeta_sigma2, nu_mu, and nu_sigma2. y should be a 1 by IJ row vector. All items not administered should have NA values in y. See package documentation for definitions and dimensions of these other objects.
obj_fun	A function that calculates predictions and log-likelihood values for the selected model (character).
int_par	Intentional parameters. That is, the parameters to optimize precision (scalar).

Value

A list with elements for omega parameter estimates (omega1), standard error of the estimates (se_omega), and the next condition to administer (next_condition).

References

Segall, D. O. (2009). Principles of Multidimensional Adaptive Testing. In W. J. van der Linden & C. A. W. Glas (Eds.), Elements of Adaptive Testing (pp. 57-75). https://doi.org/10.1007/978-0-387-85461-8_3

Examples

rda = ex5
rda$y[which(!rda$condition %in% c(3))] <- NA
cog_cat(rda = rda, obj_fun = dich_response_model, int_par = 1)

cogirt documentation built on April 3, 2025, 8:14 p.m.