sirt (version 3.9-4)

truescore.irt: Conversion of Trait Scores \(\theta\) into True Scores \(\tau ( \theta )\)

Description

This function computes the true score \(\tau=\tau(\theta)=\sum_{i=1}^I P_i(\theta)\) in a unidimensional item response model with \(I\) items. In addition, it also transforms conditional standard errors if they are provided.

Usage

truescore.irt(A, B, c=NULL, d=NULL, theta=seq(-3, 3, len=21),
    error=NULL, pid=NULL, h=0.001)

Arguments

A

Matrix or vector of item slopes. See Examples for polytomous responses.

B

Matrix or vector of item intercepts. Note that the entries in B refer to item intercepts and not to item difficulties.

c

Optional vector of guessing parameters

d

Optional vector of slipping parameters

theta

Vector of trait values

error

Optional vector of standard errors of trait

pid

Optional vector of person identifiers

h

Numerical differentiation parameter

Value

A data frame with following columns:

truescore

True scores \(\tau=\tau ( \theta )\)

truescore.error

Standard errors of true scores

percscore

Expected correct scores which is \(\tau\) divided by the maximum true score

percscore.error

Standard errors of expected correct scores

lower

The \(l\) parameter

upper

The \(u\) parameter

a

The \(a\) parameter

b

The \(b\) parameter

Details

In addition, the function \(\pi(\theta)=\frac{1}{I} \cdot \tau( \theta)\) of the expected percent score is approximated by a logistic function $$ \pi ( \theta ) \approx l + ( u - l ) \cdot invlogit ( a \theta + b ) $$

Examples

# NOT RUN {
#############################################################################
# EXAMPLE 1: Dataset with mixed dichotomous and polytomous responses
#############################################################################

data(data.mixed1)
dat <- data.mixed1

#****
# Model 1: Partial credit model
# estimate model with TAM package
library(TAM)
mod1 <- TAM::tam.mml( dat )
# estimate person parameter estimates
wmod1 <- TAM::tam.wle( mod1 )
wmod1 <- wmod1[ order(wmod1$theta), ]
# extract item parameters
A <- mod1$B[,-1,1]
B <- mod1$AXsi[,-1]
# person parameters and standard errors
theta <- wmod1$theta
error <- wmod1$error

# estimate true score transformation
dfr <- sirt::truescore.irt( A=A, B=B, theta=theta, error=error )

# plot different person parameter estimates and standard errors
par(mfrow=c(2,2))
plot( theta, dfr$truescore, pch=16, cex=.6, xlab=expression(theta), type="l",
    ylab=expression(paste( tau, "(",theta, ")" )), main="True Score Transformation" )
plot( theta, dfr$percscore, pch=16, cex=.6, xlab=expression(theta), type="l",
    ylab=expression(paste( pi, "(",theta, ")" )), main="Percent Score Transformation" )
points( theta, dfr$lower + (dfr$upper-dfr$lower)*
                stats::plogis(dfr$a*theta+dfr$b), col=2, lty=2)
plot( theta, error, pch=16, cex=.6, xlab=expression(theta), type="l",
    ylab=expression(paste("SE(",theta, ")" )), main="Standard Error Theta" )
plot( dfr$truescore, dfr$truescore.error, pch=16, cex=.6, xlab=expression(tau),
    ylab=expression(paste("SE(",tau, ")" ) ), main="Standard Error True Score Tau",
    type="l")
par(mfrow=c(1,1))

# }
# NOT RUN {
#****
# Model 2: Generalized partial credit model
mod2 <- TAM::tam.mml.2pl( dat, irtmodel="GPCM")
# estimate person parameter estimates
wmod2 <- TAM::tam.wle( mod2 )
# extract item parameters
A <- mod2$B[,-1,1]
B <- mod2$AXsi[,-1]
# person parameters and standard errors
theta <- wmod2$theta
error <- wmod2$error
# estimate true score transformation
dfr <- sirt::truescore.irt( A=A, B=B, theta=theta, error=error )

#############################################################################
# EXAMPLE 2: Dataset Reading data.read
#############################################################################
data(data.read)

#****
# Model 1: estimate difficulty + guessing model
mod1 <- sirt::rasch.mml2( data.read, fixed.c=rep(.25,12) )
mod1$person <- mod1$person[ order( mod1$person$EAP), ]
# person parameters and standard errors
theta <- mod1$person$EAP
error <- mod1$person$SE.EAP
A <- rep(1,12)
B <- - mod1$item$b
c <- rep(.25,12)
# estimate true score transformation
dfr <- sirt::truescore.irt( A=A, B=B, theta=theta, error=error,c=c)

plot( theta, dfr$percscore, pch=16, cex=.6, xlab=expression(theta), type="l",
    ylab=expression(paste( pi, "(",theta, ")" )), main="Percent Score Transformation" )
points( theta, dfr$lower + (dfr$upper-dfr$lower)*
             stats::plogis(dfr$a*theta+dfr$b), col=2, lty=2)

#****
# Model 2: Rasch model
mod2 <- sirt::rasch.mml2( data.read  )
# person parameters and standard errors
theta <- mod2$person$EAP
error <- mod2$person$SE.EAP
A <- rep(1,12)
B <- - mod2$item$b
# estimate true score transformation
dfr <- sirt::truescore.irt( A=A, B=B, theta=theta, error=error )
# }