C-Test Datasets
Some datasets of C-tests are provided. The dataset data.g308
was used in Schroeders, Robitzsch and Schipolowski (2014).
data(data.g308)
The dataset data.g308
is a C-test containing 20 items and is
used in Schroeders, Robitzsch and Schipolowski (2014) and is of the
following format
'data.frame': 747 obs. of 21 variables:
$ id : int 1 2 3 4 5 6 7 8 9 10 ...
$ G30801: int 1 1 1 1 1 0 0 1 1 1 ...
$ G30802: int 1 1 1 1 1 1 1 1 1 1 ...
$ G30803: int 1 1 1 1 1 1 1 1 1 1 ...
$ G30804: int 1 1 1 1 1 0 1 1 1 1 ...
[...]
$ G30817: int 0 0 0 0 1 0 1 0 1 0 ...
$ G30818: int 0 0 1 0 0 0 0 1 1 0 ...
$ G30819: int 1 1 1 1 0 0 1 1 1 0 ...
$ G30820: int 1 1 1 1 0 0 0 1 1 0 ...
Schroeders, U., Robitzsch, A., & Schipolowski, S. (2014). A comparison of different psychometric approaches to modeling testlet structures: An example with C-tests. Journal of Educational Measurement, 51(4), 400-418.
## Not run: ############################################################################# # EXAMPLE 1: Dataset G308 from Schroeders et al. (2014) ############################################################################# data(data.g308) dat <- data.g308 library(TAM) library(sirt) library(combinat) # define testlets testlet <- c(1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 5, 5, 6, 6, 6) #**************************************** #*** Model 1: Rasch model mod1 <- TAM::tam.mml(resp=dat[,-1], pid=dat[,1], control=list(maxiter=300, snodes=1500)) summary(mod1) #**************************************** #*** Model 2: Rasch testlet model # testlets are dimensions, assign items to Q-matrix TT <- length(unique(testlet)) Q <- matrix(0, nrow=ncol(dat)-1, ncol=TT + 1) Q[,1] <- 1 # First dimension constitutes g-factor for (tt in 1:TT){Q[testlet==tt, tt+1] <- 1} # In a testlet model, all dimensions are uncorrelated among # each other, that is, all pairwise correlations are set to 0, # which can be accomplished with the "variance.fixed" command variance.fixed <- cbind(t( combinat::combn(TT+1,2)), 0) mod2 <- TAM::tam.mml(resp=dat[,-1], pid=dat[,1], Q=Q, variance.fixed=variance.fixed, control=list(snodes=1500, maxiter=300)) summary(mod2) #**************************************** #*** Model 3: Partial credit model scores <- list() testlet.names <- NULL dat.pcm <- NULL for (tt in 1:max(testlet) ){ scores[[tt]] <- rowSums (dat[,-1][, testlet==tt, drop=FALSE]) dat.pcm <- c(dat.pcm, list(c(scores[[tt]]))) testlet.names <- append(testlet.names, paste0("testlet",tt) ) } dat.pcm <- as.data.frame(dat.pcm) colnames(dat.pcm) <- testlet.names mod3 <- TAM::tam.mml(resp=dat.pcm, control=list(snodes=1500, maxiter=300) ) summary(mod3) #**************************************** #*** Model 4: Copula model mod4 <- sirt::rasch.copula2 (dat=dat[,-1], itemcluster=testlet) summary(mod4) ## End(Not run)
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