TAM Objects — IRT.itemfit.tam" />
IRT.itemfit.Rd
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
.
# 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 | Object of class |
---|---|
method | Requested method for item fit calculation. Currently,
only the RMSD fit statistic (formerly labeled as the RMSEA statistic,
see |
... | 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 ) }