simCAT {simCAT} | R Documentation |
CAT simulation
Description
A CAT simulation with dichotomous items.
Usage
simCAT(
resps,
bank,
model = "3PL",
start.theta = 0,
sel.method = "MFI",
cat.type = "variable",
acceleration = 1,
met.weight = "mcclarty",
threshold = 0.3,
rmax = 1,
content.names = NULL,
content.props = NULL,
content.items = NULL,
met.content = "MCCAT",
stop = list(se = 0.3, hypo = 0.015, hyper = Inf),
progress = TRUE
)
Arguments
resps |
a matrix with responses (0 and 1). The number of columns corresponds to the number of items |
bank |
matrix with item parameters (a, b, c) |
model |
may be |
start.theta |
first theta |
sel.method |
item selection method: may be |
cat.type |
CAT with |
acceleration |
acceleration parameter. Necessary only for progressive method. |
met.weight |
the procedure to calculate the |
threshold |
threshold for |
rmax |
item maximum exposure rate |
content.names |
vector with the contents of the test |
content.props |
desirable proportion of each content in test, in
the same order of |
content.items |
vector indicating the content of each item |
met.content |
content balancing method: |
stop |
list with stopping rule and thresholds
|
progress |
shows progress bar |
Details
For details about formula of selection methods, see select.item
.
Value
a list with five elements
-
score
estimated theta -
convergence
TRUE
if the application ended before reaching the maximum test length -
theta.history
estimated theta after each item administration -
se.history
standard error after each item administration -
prev.resps
previous responses (administered items)
Author(s)
Alexandre Jaloto
References
Barrada, J. R., Olea, J., Ponsoda, V., & Abad, F. J. (2008). Incorporating randomness in the Fisher information for improving item-exposure control in CATs. British Journal of Mathematical and Statistical Psychology, 61(2), 493–513. 10.1348/000711007X230937
Leroux, A. J., & Dodd, B. G. (2016). A comparison of exposure control procedures in CATs using the GPC model. The Journal of Experimental Education, 84(4), 666–685. 10.1080/00220973.2015.1099511
Magis, D., & Barrada, J. R. (2017). Computerized adaptive testing with R: recent updates of the package catR. Journal of Statistical Software, 76(Code Snippet 1). 10.18637/jss.v076.c01
McClarty, K. L., Sperling, R. A., & Dodd, B. G. (2006). A variant of the progressive-restricted item exposure control procedure in computerized adaptive testing. Annual Meeting of the American Educational Research Association, San Francisco
Examples
set.seed(1)
n.items <- 50
pars <- data.frame(
a = rlnorm(n.items),
b = rnorm(n.items),
c = rbeta(n.items, 5, 17),
d = 1)
# thetas
theta <- rnorm(100)
# simulate responses
resps <- gen.resp(theta, pars[,1:3])
results <- simCAT(resps = resps,
bank = pars[,1:3],
start.theta = 0,
sel.method = 'MFI',
cat.type = 'variable',
threshold = .3,
stop = list(se = .3, max.items = 10))
eval <- cat.evaluation(
results = results,
true.scores = theta,
item.name = paste0('I', 1:nrow(pars)),
rmax = 1)
#### 3 replications
replications <- 3
# simulate responses
set.seed(1)
resps <- list()
for(i in 1:replications)
resps[[i]] <- gen.resp(theta, pars[,1:3])
# CAT
results <- list()
for (rep in 1:replications)
{
print(paste0('replication: ', rep, '/', replications))
results[[rep]] <- simCAT(
resps = resps[[rep]],
bank = pars[,1:3],
start.theta = 0,
sel.method = 'MFI',
cat.type = 'variable',
threshold = .3,
stop = list(se = .5, max.items = 10))
}
eval <- cat.evaluation(
results = results,
true.scores = theta,
item.name = paste0('I', 1:nrow(pars)),
rmax = 1)