Classification Accuracy in the Rasch Model
class.accuracy.rasch.Rd
This function computes the classification accuracy in the Rasch model for the maximum likelihood (person parameter) estimate according to the method of Rudner (2001).
Arguments
- cutscores
Vector of cut scores
- b
Vector of item difficulties
- meantheta
Mean of the trait distribution
- sdtheta
Standard deviation of the trait distribution
- theta.l
Discretized theta distribution
- n.sims
Number of simulated persons in a data set. The default is 0 which means that no simulation is performed.
Value
A list with following entries:
- class.stats
Data frame containing classification accuracy statistics. The column
agree0
refers to absolute agreement,agree1
to the agreement of at most a difference of one level.- class.prob
Probability table of classification
References
Rudner, L.M. (2001). Computing the expected proportions of misclassified examinees. Practical Assessment, Research & Evaluation, 7(14).
Examples
#############################################################################
# EXAMPLE 1: Reading dataset
#############################################################################
data( data.read, package="sirt")
dat <- data.read
# estimate the Rasch model
mod <- sirt::rasch.mml2( dat )
# estimate classification accuracy (3 levels)
cutscores <- c( -1, .3 ) # cut scores at theta=-1 and theta=.3
sirt::class.accuracy.rasch( cutscores=cutscores, b=mod$item$b,
meantheta=0, sdtheta=mod$sd.trait,
theta.l=seq(-4,4,len=200), n.sims=3000)
## Cut Scores
## [1] -1.0 0.3
##
## WLE reliability (by simulation)=0.671
## WLE consistency (correlation between two parallel forms)=0.649
##
## Classification accuracy and consistency
## agree0 agree1 kappa consistency
## analytical 0.68 0.990 0.492 NA
## simulated 0.70 0.997 0.489 0.599
##
## Probability classification table
## Est_Class1 Est_Class2 Est_Class3
## True_Class1 0.136 0.041 0.001
## True_Class2 0.081 0.249 0.093
## True_Class3 0.009 0.095 0.294