ccov.np: Nonparametric Estimation of Conditional Covariances of Item...

View source: R/ccov.np.R

ccov.npR Documentation

Nonparametric Estimation of Conditional Covariances of Item Pairs

Description

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).

Usage

ccov.np(data, score, bwscale=1.1, thetagrid=seq(-3, 3, len=200),
    progress=TRUE, scale_score=TRUE, adjust_thetagrid=TRUE, smooth=TRUE,
    use_sum_score=FALSE, bias_corr=TRUE)

Arguments

data

An N \times I data frame of dichotomous responses. Missing responses are allowed.

score

An ability estimate, e.g. the WLE

bwscale

Bandwidth factor for calculation of conditional covariance. The bandwidth used in the estimation is bwscale times N^{-1/5}.

thetagrid

A vector which contains theta values where conditional covariances are evaluated.

progress

Display progress?

scale_score

Logical indicating whether score should be z standardized in advance of the calculation of conditional covariances

adjust_thetagrid

Logical indicating whether thetagrid should be adjusted if observed values in score are outside of thetagrid.

smooth

Logical indicating whether smoothing should be applied for conditional covariance estimation

use_sum_score

Logical indicating whether sum score should be used. With this option, the bias corrected conditional covariance of Zhang and Stout (1999) is used.

bias_corr

Logical indicating whether bias correction (Zhang & Stout, 1999) should be utilized if use_sum_score=TRUE.

Note

This function is used in conf.detect and expl.detect.

References

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/014662169602000403")}

Zhang, J., & Stout, W. (1999). Conditional covariance structure of generalized compensatory multidimensional items. Psychometrika, 64(2), 129-152. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF02294532")}

Examples

## 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"])

## End(Not run)

alexanderrobitzsch/sirt documentation built on April 23, 2024, 2:31 p.m.