This function estimates conditional covariances of itempairs
(Stout, Habing, Douglas & Kim, 1996; Zhang & Stout,
1999a). The function is used for the estimation of the DETECT index.
The ccov.np
function has the (default) option to smooth item response
functions (argument smooth
) in the computation of conditional covariances
(Douglas, Kim, Habing, & Gao, 1998).
ccov.np(data, score, bwscale=1.1, thetagrid=seq(-3, 3, len=200),
progress=TRUE, scale_score=TRUE, adjust_thetagrid=TRUE, smooth=TRUE)
An
An ability estimate, e.g. the WLE
Bandwidth factor for calculation of conditional covariance. The bandwidth
used in the estimation is bwscale
times
A vector which contains theta values where conditional covariances are evaluated.
Display progress?
Logical indicating whether score
should be z standardized in advance of the calculation of
conditional covariances
Logical indicating whether thetagrid
should be
adjusted if observed values in score
are outside of thetagrid
.
Logical indicating whether smoothing should be applied for conditional covariance estimation
Douglas, J., Kim, H. R., Habing, B., & Gao, F. (1998). Investigating local dependence with conditional covariance functions. Journal of Educational and Behavioral Statistics, 23(2), 129-151. 10.3102/10769986023002129
Stout, W., Habing, B., Douglas, J., & Kim, H. R. (1996). Conditional covariance-based nonparametric multidimensionality assessment. Applied Psychological Measurement, 20(4), 331-354. 10.1177/014662169602000403
Zhang, J., & Stout, W. (1999). Conditional covariance structure of generalized compensatory multidimensional items. Psychometrika, 64(2), 129-152. 10.1007/BF02294532
# NOT RUN {
#############################################################################
# EXAMPLE 1: data.read | different settings for computing conditional covariance
#############################################################################
data(data.read, package="sirt")
dat <- data.read
#* fit Rasch model
mod <- sirt::rasch.mml2(dat)
score <- sirt::wle.rasch(dat=dat, b=mod$item$b)$theta
#* ccov with smoothing
cmod1 <- sirt::ccov.np(data=dat, score=score, bwscale=1.1)
#* ccov without smoothing
cmod2 <- sirt::ccov.np(data=dat, score=score, smooth=FALSE)
#- compare results
100*cbind( cmod1$ccov.table[1:6, "ccov"], cmod2$ccov.table[1:6, "ccov"])
# }
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