cog_cat_sim: Perform Simulated Computerized Adaptive Testing

In cogirt: Cognitive Testing Using Item Response Theory

Perform Simulated Computerized Adaptive Testing

Description

This function performs simulated adapting testing using the D-optimality criterion (Segall, 2009) which allows the user to focus on a subset of intentional abilities (or traits).

Usage

cog_cat_sim(
  data = NULL,
  model = NULL,
  guessing = NULL,
  contrast_codes = NULL,
  num_conditions = NULL,
  num_contrasts = NULL,
  constraints = NULL,
  key = NULL,
  omega = NULL,
  item_disc = NULL,
  item_int = NULL,
  conditions = NULL,
  int_par = NULL,
  start_conditions = NULL,
  max_conditions = Inf,
  omit_conditions = NULL,
  min_se = -Inf,
  link = "probit",
  verbose = TRUE
)

Arguments

data	A matrix of item responses (K by IJ). Rows should contain dichotomous responses (1 or 0) for the items indexed by each column.
model	An IRT model name. The options are "1p" for the one-parameter model, "2p" for the two-parameter model, "3p" for the three-parameter model, or "sdt" for a signal detection-weighted model.
guessing	Either a single numeric guessing value or a matrix of item guessing parameters (IJ by 1). This argument is only used when model = '3p'.
contrast_codes	Either a matrix of contrast codes (JM by MN) or the name in quotes of a R stats contrast function (i.e., "contr.helmert", "contr.poly", "contr.sum", "contr.treatment", or "contr.SAS"). If using the R stats contrast function items in the data matrix must be arranged by condition.
num_conditions	The total number of possible conditions (required if using the R stats contrast function or when constraints = TRUE).
num_contrasts	The number of contrasts, including intercept (required if using the R stats contrast function or when constraints = TRUE).
constraints	Either a logical (TRUE or FALSE) indicating that item parameters should be constrained to be equal over the J conditions, or a 1 by I vector of items that should be constrained to be equal across conditions.
key	An item key vector where 1 indicates a target and 2 indicates a distractor (IJ). Required when model = 'sdt'.
omega	A matrix of true omega parameters if known. These are estimated using the complete data if not supplied by the user.
item_disc	A matrix of item discrimination parameters if known. These are estimated using the complete data if not supplied by the user.
item_int	A matrix of item intercept parameters if known. These are estimated using the complete data if not supplied by the user.
conditions	A list of experimental conditions that the adaptive testing algorithm will choose from. The word "conditions" here refers to a single item or a group of items that should be administered together before the next iteration of adaptive testing. For cognitive experiments, multiple conditions can be assigned the same experimental level (e.g., memory load level).
int_par	The index of the intentional parameters, i.e., the column of the experimental effects matrix (omega) that should be optimized.
start_conditions	A vector of condition(s) completed prior to the onset of adaptive testing.
max_conditions	The maximum number of conditions to administer before terminating adaptive testing. If max_conditions is specified, min_se should not be. Note that this is the number of additional conditions to administer beyond the starting conditions.
omit_conditions	A vector of conditions to be omitted from the simulation.
min_se	The minimum standard error of estimate needed to terminate adaptive testing. If min_se is specified, max_conditions should not be.
link	The name ("logit" or "probit") of the link function to be used in the model.
verbose	Logical (TRUE or FALSE) indicating whether to print progress.

Value

A list with elements with the model used (model), true omega parameters (omega), various simulation parameters, final omega estimates (omega1) and information matrices (info1_omega), ongoing estimates of omega (ongoing_omega_est) and standard error of the estimates (ongoing_se_omega), and completed conditions (completed_conditions).

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

sim_res <- cog_cat_sim(data = ex3$y, model = 'sdt', guessing = NULL,
                       contrast_codes = "contr.poly", num_conditions = 10,
                       num_contrasts = 2, constraints = NULL, key = ex3$key,
                       omega = ex3$omega, item_disc = ex3$lambda,
                       item_int = ex3$nu, conditions = ex3$condition,
                       int_par = c(1, 2), start_conditions = 3,
                       max_conditions = 3, link = "probit")
summary(sim_res)
plot(sim_res)

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