TAM (version 4.2-21)

IRT.itemfit.tam: RMSD Item Fit Statistics for TAM Objects

Description

Computes the RMSD item fit statistic (formerly labeled as RMSEA; Yamamoto, Khorramdel, & von Davier, 2013) for fitted objects in the TAM package, see CDM::IRT.itemfit and CDM::IRT.RMSD.

Usage

# S3 method for tam.mml
IRT.itemfit(object, method="RMSD", ...)
# S3 method for tam.mml.2pl
IRT.itemfit(object, method="RMSD", ...)
# S3 method for tam.mml.mfr
IRT.itemfit(object, method="RMSD", ...)
# S3 method for tam.mml.3pl
IRT.itemfit(object, method="RMSD", ...)

Arguments

object

Object of class tam.mml, tam.mml.2pl, tam.mml.mfr or tam.mml.3pl.

method

Requested method for item fit calculation. Currently, only the RMSD fit statistic (formerly labeled as the RMSEA statistic, see CDM::IRT.RMSD) can be used.

...

Further arguments to be passed.

References

Yamamoto, K., Khorramdel, L., & von Davier, M. (2013). Scaling PIAAC cognitive data. In OECD (Eds.). Technical Report of the Survey of Adults Skills (PIAAC) (Ch. 17). Paris: OECD.

See Also

CDM::IRT.itemfit, CDM::IRT.RMSD

Examples

if (FALSE) {
#############################################################################
# EXAMPLE 1: RMSD item fit statistic data.read
#############################################################################

library(sirt)
data(data.read,package="sirt")
dat <- data.read

#*** fit 1PL model
mod1 <- TAM::tam.mml( dat )
summary(mod1)

#*** fit 2PL model
mod2 <- TAM::tam.mml.2pl( dat )
summary(mod2)

#*** assess RMSEA item fit
fmod1 <- IRT.itemfit(mod1)
fmod2 <- IRT.itemfit(mod2)
# summary of fit statistics
summary( fmod1 )
summary( fmod2 )

#############################################################################
# EXAMPLE 2: Simulated 2PL data and fit of 1PL model
#############################################################################

set.seed(987)
N <- 1000    # 1000 persons
I <- 10      # 10 items
# define item difficulties and item slopes
b <- seq(-2,2,len=I)
a <- rep(1,I)
a[c(3,8)] <- c( 1.7, .4 )
# simulate 2PL data
dat <- sirt::sim.raschtype( theta=rnorm(N), b=b, fixed.a=a)

# fit 1PL model
mod <- TAM::tam.mml( dat )

# RMSEA item fit
fmod <- IRT.itemfit(mod)
round( fmod, 3 )
}