class.accuracy.rasch: Classification Accuracy in the Rasch Model

View source: R/class.accuracy.rasch.R

class.accuracy.raschR Documentation

Classification Accuracy in the Rasch Model

Description

This function computes the classification accuracy in the Rasch model for the maximum likelihood (person parameter) estimate according to the method of Rudner (2001).

Usage

class.accuracy.rasch(cutscores, b, meantheta, sdtheta, theta.l, n.sims=0)

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).

See Also

Classification accuracy of other IRT models can be obtained with the R package cacIRT.

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

alexanderrobitzsch/sirt documentation built on Dec. 3, 2022, 6:18 p.m.