CDM (version 2.7-7)

gdd: Generalized Distance Discriminating Method

Description

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.

Usage

gdd(data, q.matrix, theta, b, a, skillclasses = NULL)

Arguments

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

Value

• 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

Details

Note that the parameters in the arguments follow the item response model $$logit P( X_{nj} = 1 | \theta_n ) = b_j + a_j \theta_n$$ which is employed in the gdm function.

References

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.

Examples

#############################################################################
# EXAMPLE 1: GDD for sim.dina
#############################################################################	

data(sim.dina)
data(sim.qmatrix)

q.matrix <- sim.qmatrix
data <- sim.dina

# estimate 1PL (use irtmodel="2PL" for 2PL estimation)
mod <- 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 <- gdd( data , q.matrix , theta=theta , b=b, a=a )