data.pisa00R.RdThis is a sub-dataset of the PISA 2000 of German students including 26 items of the reading test. The 26 items was analyzed in Chen and de la Torre (2014) and a subset of 20 items was analyzed in Chen and Chen (2016).
The format of the dataset data.pisa00R.ct
(Chen & de la Torre, 2014) is:
List of 3 $ data :'data.frame': 1095 obs. of 111 variables: .. [list output truncated] $ q.matrix: num [1:26, 1:8] 0 1 0 0 0 1 0 0 0 1 ... ..- attr(*, "dimnames")=List of 2 $ skills : chr [1:8] "Locating information" ...
The format of the dataset data.pisa00R.cc
(Q-matrix in Chen and Chen, 2016)
List of 2 $ q.matrix:'data.frame': 20 obs. of 5 variables: ..$ A1: num [1:20] 1 1 0 0 1 1 1 0 0 0 ... ..$ A2: num [1:20] 0 0 0 1 0 1 1 1 1 1 ... ..$ A3: num [1:20] 1 1 0 1 1 0 1 0 1 0 ... ..$ A4: num [1:20] 0 1 1 1 0 0 0 0 0 0 ... ..$ A5: num [1:20] 0 0 1 0 0 0 0 1 0 1 ... $ skills : Named chr [1:5] "Identifying Explicit Information" ... ..- attr(*, "names")=chr [1:5] "A1" "A2" "A3" "A4" ...
Chen, H., & Chen, J. (2016). Exploring reading comprehension skill relationships through the G-DINA model. Educational Psychology, 36(6), 1049-1064.
Chen, J., & de la Torre, J. (2014). A procedure for diagnostically modeling extant large-scale assessment data: the case of the programme for international student assessment in reading. Psychology, 5(18), 1967-1978.
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# EXAMPLE 1: PISA items from Chen and de la Torre (2014)
# dichotomize item responses
#############################################################################
data(data.pisa00R.ct, package="CDM")
dat <- data.pisa00R.ct$data
Q <- data.pisa00R.ct$q.matrix
resp <- dat[, rownames(Q)]
#** extract item-wise maximum
maxK <- apply( resp, 2, max, na.rm=TRUE )
#** dichotomize response data
resp1 <- resp
for (ii in seq(1,ncol(resp)) ){
resp1[,ii] <- 1 * ( resp[,ii]==maxK[ii] )
}