data.g308: C-Test Datasets

In alexanderrobitzsch/sirt: Supplementary Item Response Theory Models

C-Test Datasets

Description

Some datasets of C-tests are provided. The dataset data.g308 was used in Schroeders, Robitzsch and Schipolowski (2014).

Usage

data(data.g308)

Format

• 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 ...
  

References

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.

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Dataset G308 from Schroeders et al. (2014)
#############################################################################

data(data.g308)
dat <- data.g308

library(TAM)
library(sirt)

# 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, 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), 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( utils::combn(TT+1,2)), 0)
mod2 <- TAM::tam.mml(resp=dat, 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[, 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, itemcluster=testlet)
summary(mod4)

## End(Not run)

alexanderrobitzsch/sirt documentation built on Dec. 1, 2024, 2:18 a.m.