IRT.itemfit.RdComputes 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.
# 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", ...)Object of class tam.mml, tam.mml.2pl, tam.mml.mfr
or tam.mml.3pl.
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.
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.
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 )
}