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

Description Usage Arguments Value References See Also Examples

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

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

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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

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#############################################################################
# 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 June 27, 2021, 12:03 a.m.