Generalized Distance Discriminating Method
Performs the generalized distance discriminating method (GDD; Sun, Xin, Zhang, & de la Torre, 2013) for dichotomous data which is a method for classifying students into skill profiles based on a preliminary unidimensional calibration.
gdd(data, q.matrix, theta, b, a, skillclasses=NULL)
data |
Data frame with N \times J item responses |
q.matrix |
The Q-matrix |
theta |
Estimated person ability |
b |
Estimated item intercept from a 2PL model (see Details) |
a |
Estimated item slope from a 2PL model (see Details) |
skillclasses |
Optional matrix of skill classes used for estimation |
Note that the parameters in the arguments follow the item response model
logit P( X_{nj}=1 | θ_n )=b_j + a_j θ_n
which is employed in the gdm function.
A list with following entries
skillclass.est |
Estimated skill class |
distmatrix |
Distances for every person and every skill class |
skillspace |
Used skill space for estimation |
theta |
Used person parameter estimate |
Sun, J., Xin, T., Zhang, S., & de la Torre, J. (2013). A polytomous extension of the generalized distance discriminating method. Applied Psychological Measurement, 37, 503-521.
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# EXAMPLE 1: GDD for sim.dina
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data(sim.dina, package="CDM")
data(sim.qmatrix, package="CDM")
data <- sim.dina
q.matrix <- sim.qmatrix
# estimate 1PL (use irtmodel="2PL" for 2PL estimation)
mod <- CDM::gdm( data, irtmodel="1PL", theta.k=seq(-6,6,len=21),
decrease.increments=TRUE, conv=.001, globconv=.001)
# extract item parameters in parametrization b + a*theta
b <- mod$b[,1]
a <- mod$a[,,1]
# extract person parameter estimate
theta <- mod$person$EAP.F1
# generalized distance discriminating method
res <- CDM::gdd( data, q.matrix, theta=theta, b=b, a=a )Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.