CDM (version 1.4-16)

cdm.est.class.accuracy: Classification Reliability in a CDM

Description

This function computes the classification accuracy and consistency by the method of Cui, Gierl and Chang (2012) and by simulation. The simulation function now only works for models of class din (i.e. the mixed DINA and DINO model).

Usage

cdm.est.class.accuracy(cdmobj, n.sims = 0, seed = 987)

Arguments

cdmobj

Object of class din or gdina

n.sims

Number of simulated persons. If n.sims=0, then the number of persons in the original data is used as the sample size. In case of missing item responses, everytime this sample size is used. Note that the simulation does (up to now) only wo

seed

Simulation seed

Value

• A data frame for MLE and MAP classification reliability with following columns
• P_a
  Classification accuracy (Gierl et al., 2012)
• P_a_sim
  Classification accuracy based on simulated data
• P_c
  Classification consistency (Gierl et al., 2012)
• P_c_sim
  Classification consistency based on simulated data

Details

The item parameters and the probability distribution of latent classes is used as the basis of the simulation. Accuracy and consistency is estimated for both MLE and MAP classification estimators.

References

Cui, Y., Gierl, M. J. & Chang, H.-H. (2012). Estimating classification consistency and accuracy for cognitive diagnostic assessment. Journal of Educational Measurement, 49, 19-38.

Examples

######################################
# DINO data example

# estimate DINO model
mod <- din( sim.dino , q.matrix = sim.qmatrix , rule="DINO")
# estimate classification reliability
cdm.est.class.accuracy(  mod , n.sims=5000)
##   > cdm.est.class.accuracy(  mod , n.sims=5000)
##         P_a P_a_sim   P_c P_c_sim
##   MLE 0.668   0.661 0.583   0.541
##   MAP 0.807   0.785 0.717   0.670